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","tags":["article","deep-learning"]},{"id":2406,"title":"Audio signal analysis and Feature extraction","description":"Extract features from audio signal as a part of Speech recognition task","tags":["code","research","tutorial","library","audio","feature-engineering","speech","speech-recognition","melspectrum","mfcc"]},{"id":2405,"title":"Scientific Computing in Python: Introduction to NumPy& Matplotlib","description":"Blog article with the embedded \u201cnarrated content\u201d for NumPy and Matplotlib notes.","tags":["article","code","tutorial","matplotlib","numpy"]},{"id":2404,"title":"12 Factors of Reproducible Machine Learning in Production","description":"We took our experience to deduce 12 factors (as a nod to the 12 factor app) that build the backbone of successful ML in production.","tags":["article","code","machine-learning","production","reproducability"]},{"id":2403,"title":"Easy Data Augmentation (EDA)","description":"Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks","tags":["article","code","library","data-augmentation","natural-language-processing","eda","text-augmentation"]},{"id":2402,"title":"Adapting Text Augmentation to Industry Problems","description":"In this post I will talk about the recent advances in exploiting language models for data generation and also show how, where we can implement them in Industry.","tags":["article","data-augmentation","language-modeling","natural-language-processing","pet","cbert","text-augmentaiton"]},{"id":2400,"title":"MLJ.jl","description":"A Julia machine learning framework.","tags":["code","julia","machine-learning","library"]},{"id":2399,"title":"Image Dehazing using GMAN net","description":"Single image dehazing using the GMAN network and its implementation in Tensorflow(version 2+).","tags":["article","code","dataset","research","tensorflow","deep-learning","dehazing"]},{"id":2398,"title":"Deep Dive into TensorBoard: Tutorial With Examples","description":"There is a common business saying that you can\u2019t improve what you don\u2019t measure. 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","tags":["code","github","tutorial","scikit-learn","wandb","mlops","github-actions"]},{"id":2386,"title":"Silero Models: pre-trained enterprise-grade STT models","description":"Silero Speech-To-Text models provide enterprise grade STT in a compact form-factor for several commonly spoken languages.","tags":["article","code","dataset","notebook","paper","research","onnx","pytorch","tensorflow","library","speech","speech-recognition","speech-to-text","demo"]},{"id":2385,"title":"Beginner\u2019s Guide to Linear Regression with cuML","description":"Break down of simple & multiple linear regression and how to easily implement both in Python with RAPIDS AI\u2019s cuML","tags":["article","code","dataset","notebook","tutorial","python","linear-regression","machine-learning","regression","data-science","cuml","rapids"]},{"id":2384,"title":"Intro to Facebook Prophet","description":"Everything you need to know when starting out with Facebook\u2019s time series forecasting tool","tags":["article","code","dataset","tutorial","python","time-series","time-series-forecasting","prophet"]},{"id":2383,"title":"Beginner\u2019s Guide to BlazingSQL","description":"Everything you need to know when starting out","tags":["article","code","dataset","notebook","tutorial","python","sql","gpu","rapids","blazingsql"]},{"id":2382,"title":"Distributed SQL with Dask","description":"Scale your Python data science across multiple GPUs with BlazingSQL (w/ code + data)","tags":["article","code","dataset","notebook","tutorial","python","sql","gpu","dask","rapids","blazingsql"]},{"id":2381,"title":"PySR","description":"Simple and fast symbolic regression in Python/Julia via regularized evolution and simulated annealing.","tags":["code","paper","research","julia","python","regression","library","genetic-algorithm","arxiv:2006.11287","symbolic-regression","pysr"]},{"id":2380,"title":"Interact with PyTorch layers using Jupyter Widgets","description":"Build your understanding of PyTorch's ConvTranspose1d layer using interactive visualisations\r\n\r\n","tags":["article","code","notebook","convolutional-neural-networks","interactive","padding","stride"]},{"id":2379,"title":"ML projects ideas! 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","tags":["code","notebook","paper","research","pytorch","library","computer-vision","3d-face","face-aligment","arxiv:2009.09960"]},{"id":2359,"title":"Supermarket System","description":"A web application for supermarket to manage all details.","tags":["code","css","html","javascript","sql","web-design"]},{"id":2358,"title":"Student Dropout Prediction","description":"It is a machine learning based web app to predict whether a student get dropout from college based on his academic and financial details.","tags":["code","machine-learning","random-forests","decision-tree"]},{"id":2357,"title":"Sudoku Solver","description":"Solving Sudoku by extracting the puzzle from photo using Computer Vision and OCR and solving it.","tags":["code","machine-learning","computer-vision","optical-character-recognition"]},{"id":2356,"title":"Part 2: Deep Representations, a way towards neural style transfer","description":"A top-down approach to conceiving neural style transfer","tags":["article","code","tutorial","keras","computer-vision","wandb","neural-style-transfer","tensorflow2","wand"]},{"id":2355,"title":"\ud83d\udea7 Simple considerations for simple people building fancy NNs ","description":"I will try to highlight a few steps of my mental process when it comes to building and debugging neural networks. 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","tags":["code","paper","research","tensorflow","deep-learning","computer-vision","self-supervised-learning","arxiv:2009.12007"]},{"id":2335,"title":"GitHub CLI 1.0: All you need to know","description":"GitHub CLI basically brings GitHub to your terminal.","tags":["article","github","cli","github-cli"]},{"id":2334,"title":"Python 3.9: All You need to know","description":"The next version of Python brings a faster release schedule, performance boosts, handy new string functions, dictionary union operators, and more stable APIs.","tags":["article","python"]},{"id":2333,"title":"Machine Learning-Enabled Design of Point Defects in 2D Materials ","description":"Using deep transfer learning, machine learning, and quantum mechanical calculations we predict key properties of point defects in 2D materials.","tags":["article","code","dataset","paper","research","deep-learning","machine-learning","transfer-learning"]},{"id":2332,"title":"Recurrent Neural Networks: building GRU cells VS LSTM cells ","description":"What are the advantages of RNN\u2019s over transformers? 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","tags":["article","code","tutorial","tensorflow","computer-vision","wandb","model-optimization"]},{"id":2327,"title":"Paint with Machine Learning","description":"This web app allows you to create a landscape painting in the style of Bob Ross using a deep learning model served using a Spell model server.","tags":["article","pytorch","generative-adversarial-networks","web-design","computer-vision","image-classification","image-to-image-translation","interactive","demo"]},{"id":2326,"title":"NLP for Developers: Multilingual NLP | Rasa","description":"In this video, Rasa Developer Advocate Rachael will talk about common approaches to handle language input in more than one language.","tags":["video","multilingual","natural-language-processing","rasa"]},{"id":2325,"title":"Implementing Content-Based Image Retrieval with Siamese Networks","description":"With content-based image retrieval, we refer to the task of finding images containing attributes which are not in the image metadata, but in its visual content.","tags":["article","pytorch","siamese-networks","computer-vision","image-similarity-search","image-retrieval"]},{"id":2324,"title":"Character level language model RNN","description":"This is a commentary of the min char language model of [@karpathy](https://twitter.com/karpathy).","tags":["article","tutorial","recurrent-neural-networks"]},{"id":2323,"title":"DeepTrain","description":"Abstract away boilerplate train loop and data loading code, without making it into a black box. 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It needs both methods from Computer Vision and Natural ","tags":["code","tutorial","pytorch","convolutional-neural-networks","deep-learning","computer-vision","image-captioning","natural-language-processing"]},{"id":2190,"title":"Practical Data Ethics","description":"Course covering disinformation, bias, ethical foundations, privacy & surveillance, silicon valley ecosystem, and algorithmic colonialism.","tags":["course","surveillance","fairness","bias","ethics","ethical-ml","disinformation"]},{"id":2188,"title":"Great Expectations","description":"Always know what to expect from your data.","tags":["code","video","library","unit-tests","pandas","testing","demo","tests","great-expectations","data-quality"]},{"id":2186,"title":"AI in Medicine and Imaging - Stanford Symposium 2020","description":"Through the AIMI Symposium we hope to address gaps and barriers in the field and catalyze more evidence-based solutions to improve health for all.","tags":["article","video","health","medicine","computer-vision","medical-imaging","stanford","videos"]},{"id":2185,"title":"Language Interpretability Tool (LIT)","description":"The Language Interpretability Tool (LIT) is a visual, interactive model-understanding tool for NLP models.","tags":["code","library","interpretability","natural-language-processing"]},{"id":2184,"title":"From Hours to Seconds: 100x Faster Boosting, Bagging, & Stacking","description":"100x Faster Boosting, Bagging, and Stacking with RAPIDS cuML and Scikit-learn Machine Learning Model Ensembling.","tags":["article","code","tutorial","scikit-learn","boosting","cuml","rapids","emsembling","bagging","stacking"]},{"id":2183,"title":"Generating music in the waveform domain","description":"Overview of generative models applied to music generation.","tags":["article","autoencoders","generative-adversarial-networks","variational-autoencoders","audio","music-generation","wavenet","waveforms"]},{"id":2182,"title":"CryptoCRIT","description":"CryptoCRIT is an open-source Cryptocurrency project which facilitates a Cryptocurrency wallet for making payments. 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","tags":["article","course","tutorial","python","decorators"]},{"id":2148,"title":"txtai: AI-powered search engine","description":"AI-powered search engine.","tags":["code","transformers","library","natural-language-processing","search","faiss"]},{"id":2147,"title":"Practical Tips and Tricks for Successful Transfer Learning","description":"Training models to learn knowledge and skills from other related tasks that will transfer and boost performance on tasks of interest.","tags":["paper","research","tutorial","video","natural-language-processing","transfer-learning","pretraining","arxiv:2005.00628"]},{"id":2145,"title":"Predicting Credit Card Approvals","description":"Project provided by DataCamp. 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","tags":["code","research","finance","natural-language-processing","sentiment-analysis"]},{"id":2137,"title":"Unpopular Opinion - Data Scientists Should Be More End-to-End","description":"I believe data scientists can be more effective by being end-to-end.","tags":["article","full-stack","mlops","career"]},{"id":2136,"title":"Machine Learning Deployment: Shadow Mode","description":"\u201cHow do I test my new model in production?\u201d One answer, and a method I often employ when initially deploying models, is shadow mode.","tags":["article","production","deployment","shadow-mode"]},{"id":2135,"title":"Temporal Convolutional Networks for Time-Series","description":"We introduce several novels using TCN, including improving traffic prediction, sound event localization & detection, and probabilistic forecasting.","tags":["article","temporal-cnn","forecasting","time-series","cnn","detection","localization"]},{"id":2134,"title":"Machine Learning of Sets","description":"We will consider set-to-vector, vector-to-set, and set-to-set problems and provide implementations of simple algorithms in JAX and Haiku.","tags":["article","jax","machine-learning","haiku","iid","sets"]},{"id":2132,"title":"Adversarial robustness as a prior for better transfer learning","description":"We find that adversarially robust models, while less accurate, often perform better than their standard-trained counterparts when used for transfer learning.","tags":["article","code","paper","research","transfer-learning","adversarial-learning","imagenet","arxiv:2007.08489"]},{"id":2131,"title":"How to Trust Your Deep Learning Code","description":"We will focus on how to write reusable unit tests, so that you \u201cDon\u2019t repeat yourself\u201d.","tags":["article","code","tutorial","unit-tests","testing"]},{"id":2128,"title":"AlphaPy: AutoML for Data Scientists and Speculators","description":"AlphaPy is an automated machine learning (AutoML) Python framework for Data Scientists and Speculators.","tags":["code","keras","scikit-learn","sports","automl","alphapy"]},{"id":2127,"title":"Number Prediction","description":"Deep learning model made from scratch,Trained on MNIST dataset","tags":["code","tutorial","deep-learning"]},{"id":2126,"title":"Machine Learning Monthly July 2020","description":"A collection of the coolest machine learning resources for July 2020, including: GPT-3, self-supervised learning, AutoML-Zero and more! 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And if you have a good detector, simple methods can be pretty effective.","tags":["article","code","tutorial","pytorch","computer-vision","object-tracking"]},{"id":2031,"title":"NeuralQA - API and Visual Interface for Extractive QA","description":"A Usable Library for Question Answering on Large Datasets with BERT","tags":["code","attention","bert","transformers","library","natural-language-processing","question-answering","visualization"]},{"id":2030,"title":"A Few Favorite Recipes in Computer Vision & Deep Learning","description":"This blog post enlists a few of my favorite recipes in deep learning in the context of computer vision (as of August 2020).","tags":["article","research","deep-learning","computer-vision","representation-learning","self-supervised-learning","simclr","contrastive-learning"]},{"id":2029,"title":"Self-supervised representation learning on videos","description":"Everything you need to know about video-based approaches on self-supervised learning","tags":["article","video","representation-learning","self-supervised-learning"]},{"id":2028,"title":"Which machine learning algorithm to choose?","description":"in this article, we show you structured methodology and guidelines which will help you to select the best algorithm for your use.","tags":["article","machine-learning","regression","classification"]},{"id":2027,"title":"Nearest Celebrity Face","description":"Implementation of FaceNet: A Unified Embedding for Face Recognition and Clustering to find the celebrity whose face matches the closest to yours. 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Let's see the possible options we have to deal with it.","tags":["article","code","dataset","tutorial","scikit-learn","missing-data"]},{"id":2020,"title":"Text Summarization using TF-IDF Algorithm","description":"This Article explains the TF-IDF algorithm and shows the implemtnation from scratch to summarize the text.","tags":["article","code","natural-language-processing","text-summarization","demo","tf-idf"]},{"id":2018,"title":"Training CNN with Mixed Precision in PyTorch 1.6","description":"Simple Example on MNIST dataset illustrating Mixed Precision Training.\r\n","tags":["code","tutorial","pytorch","convolutional-neural-networks","deep-learning"]},{"id":2017,"title":"AutoML clone","description":"AutoMl clone that lets you build and deploy ML models with 0 lines of code","tags":["code","fastai","keras","convolutional-neural-networks","machine-learning","automl","fastap"]},{"id":2016,"title":"Meme Classifier Using TFlite and flutter","description":"Meme classifier using fine tuned mobilenet. 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","tags":["code","research","computer-vision","object-detection"]},{"id":2011,"title":"UFOD: A Unified Framework for Object Detection","description":"UFOD is an open-source framework that enables the training and comparison of object detection models on custom datasets using different underlying frameworks an","tags":["code","automl","computer-vision","object-detection"]},{"id":2010,"title":"FrImCla: A framework for image classification","description":"\r\nFrImCla is an open-source framework for Image Classification using traditional and deep learning techniques. It supports a wide variety of deep learning and c","tags":["code","paper","research","computer-vision","image-classification","transfer-learning"]},{"id":2009,"title":"ATLASS: AutoML using Transfer and Semi-Supervised Learning","description":"This repository includes the code, application, and notebooks for the work \"AutoML using Transfer and Semi-Supervised Learning\". The tools presented here can be","tags":["code","paper","research","automl","computer-vision","image-classification","semi-supervised-learning","transfer-learning"]},{"id":2008,"title":"Extension to block NSFW content using AI","description":"NSFW Filter is an extension that blocks NSFW content from your browser.\r\nIt uses a computer vision model to detect NSFW content and hides it from the user.","tags":["code","javascript","tensorflow","tensorflow-js","convolutional-neural-networks","deep-learning","machine-learning","web-services","computer-vision","transfer-learning","demo"]},{"id":2007,"title":"CLoDSA: A Tool for Augmentation in Computer Vision tasks","description":"CLoDSA is an open-source image augmentation library for object classification, localization, detection, semantic segmentation and instance segmentation. It supp","tags":["code","paper","research","library","computer-vision","data-augmentation","object-classification","object-detection","segmentation"]},{"id":2006,"title":"Deep Learning Techniques for NLP in Healthcare","description":"A talk discussing the recent advancements of deep learning to facilitate the adaption of NLP in the healthcare domain.","tags":["tutorial","video","deep-learning","health","natural-language-processing"]},{"id":2005,"title":"Social Distance Detection","description":"If people are very close to each other, a red bounding box is displayed around them indicating that they are not maintaining social distance.","tags":["code","tutorial","video","python","computer-vision","opencv","inference","social-distancing"]},{"id":2004,"title":"Cancer Diagnosis using ML Techniques","description":"In this project we will be discussing how to predict the effect of Genetic Variants to enable Personalized Medicine for various types of cancer using ML algos.\r\n","tags":["article","code","dataset","logistic-regression","machine-learning","random-forests","regression","support-vector-machines","k-nearest-neighbors","decision-tree","cancer"]},{"id":2003,"title":"What's New in PyTorch 1.6","description":"A brief overview of new and interesting features in PyTorch 1.6\r\nIt contains byte-sized and working examples to show these features.","tags":["code","tutorial","pytorch","library"]},{"id":2002,"title":"Multi-target in Albumentations","description":"Many images, many masks, bounding boxes, and key points. 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","tags":["article","code","notebook","video","tensorflow","computer-vision","object-detection","tflite"]},{"id":1992,"title":"Time series forecasting","description":"A thorough introduction to time series forecasting using TensorFlow.","tags":["article","code","notebook","tutorial","keras","tensorflow","forecasting","time-series"]},{"id":1991,"title":"2020 Machine Learning Roadmap","description":"A beginner mind map (and presentation) connecting many of the most foundational concepts in machine learning.","tags":["code","tutorial","machine-learning","data-science","beginner","introduction"]},{"id":1990,"title":"scikit-learn-lambda","description":"A cost-efficient toolkit for deploying scikit-learn models for realtime HTTP inference on AWS Lambda.","tags":["article","code","aws","scikit-learn","serverless"]},{"id":1989,"title":"Interpretability in ML: A Broad Overview","description":"An overview of the sub-field of machine learning interpretability, with example models and graphics.","tags":["article","code","machine-learning","interpretability","ai-safety"]},{"id":1988,"title":"Self-Supervised Learning - PyTorch Lightning","description":"This bolts module houses a collection of all self-supervised learning models.","tags":["code","pytorch","library","self-supervised-learning","pytorch-lightning","simclr","contrastive-learning","moco"]},{"id":1987,"title":"SimCLR in Keras","description":"Tensorflow-Keras Implementation of SimCLR","tags":["code","dataset","paper","research","keras","tensorflow","self-supervised-learning","simclr","contrastive-learning","demo","arxiv:2002.05709","keras-models","t-sne"]},{"id":1986,"title":"Behavioral Testing of NLP models with CheckList","description":"An overview of the \u201cCheckList\u201d framework for fine-grained evaluation of NLP models","tags":["article","tutorial","natural-language-processing","nlp"]},{"id":1985,"title":"Temporal Graph Networks","description":"In this post, we describe Temporal Graph Network, a generic framework for deep learning on dynamic graphs.","tags":["article","code","paper","research","tutorial","graph-neural-networks","graphs","temporal-graph-networks","arxiv:2006.10637"]},{"id":1984,"title":"How GPT3 Works - Visualizations and Animations","description":"A compilation of my threads explaining GPT3. 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It is different from most of the tutorials available on the internet.","tags":["api","code","tutorial","django","machine-learning"]},{"id":1959,"title":"TeachEasy: Web app for Text Summarization & Q/A generation","description":"An intuitive Streamlit based web app for Text Summarization and Question Answer generation so as to reduce the work for School teachers.","tags":["code","natural-language-processing","paraphrase-identification","question-answering","question-generation","text-summarization","streamlit"]},{"id":1958,"title":"Automated Machine Learning","description":"\u2728 Automated Machine Learning Python package designed to save time for a data scientist \ud83d\ude0e ","tags":["code","machine-learning","library"]},{"id":1957,"title":"The Beginner's Guide to Dimensionality Reduction","description":"Explore the methods that data scientists use to visualize high-dimensional data.","tags":["code","tutorial","dimensionality-reduction","tsne","principal-component-analysis","interactive","umap"]},{"id":1956,"title":"Video Prediction using ConvLSTM Autoencoder (PyTorch)","description":"A simple implementation of the Convolutional-LSTM model.","tags":["article","tutorial","pytorch","autoencoders","convolutional-neural-networks","lstm","pytorch-lightning","video-prediction"]},{"id":1955,"title":"Shape and Viewpoint without Keypoints","description":"Recover the 3D shape, pose and texture from a single image, trained on an image collection without any ground truth 3D shape, multi-view, camera viewpoints.","tags":["article","code","paper","research","3d","computer-vision","unsupervised-learning","pascal-3d","arxiv:2007.10982"]},{"id":1954,"title":"Azure ML","description":"MLOps using Azure ML.","tags":["code","azure","library","mlops","serving","ci-cd"]},{"id":1953,"title":"BentoML","description":"BentoML is an open-source framework for high-performance ML model serving.","tags":["code","library","production","serving","bentoml","ci-cd"]},{"id":1952,"title":"TensorFlow Serving","description":"A flexible, high-performance serving system for machine learning models, designed for production environments. 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","tags":["tutorial","video","production","torchserve","serving"]},{"id":1883,"title":"Torchfunc","description":"PyTorch functions & utilities to make your deep learning life easier\r\n\r\n","tags":["code","pytorch","library","utilities","functions"]},{"id":1882,"title":"Object Detection with RetinaNet","description":"Implementing RetinaNet: Focal Loss for Dense Object Detection.","tags":["article","notebook","paper","research","tutorial","keras","tensorflow","computer-vision","object-detection","retinanet","arxiv:1708.02002"]},{"id":1880,"title":"AdapterHub: A Framework for Adapting Transformers","description":"Huggingface Transformers + Adapters","tags":["code","paper","research","huggingface","transformers","library","natural-language-processing","adapters","arxiv:2007.07779"]},{"id":1879,"title":"Cross-lingual Transfer Learning - Sebastian Ruder","description":"An overview of approaches that transfer knowledge across languages and enable us to scale NLP models to more of the world's 7,000 languages.","tags":["article","tutorial","video","cross-lingual","natural-language-processing","transfer-learning"]},{"id":1878,"title":"PyPika - Python Query Builder","description":"A python SQL query builder that exposes the full richness of the SQL language using a syntax that reflects the resulting query.","tags":["code","sql","library","pypika"]},{"id":1877,"title":"An Overview of Distributed Training of Deep Learning Models","description":"Overview of the different techniques that are used by contemporary distributed DL systems and discuss their influence and implications on the training process.","tags":["paper","research","training","distributed-training","overview","arxiv:2007.03970"]},{"id":1876,"title":"Predict the hourly output power of a Combined Cycle Power Plant.","description":"In this project I have explored the data collected from a Combined Cycle Power Plant over 6 years. 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","tags":["article","code","tutorial","tensorflow","deep-learning","generative-adversarial-networks","machine-learning","computer-vision","image-to-image-translation","tensorflow-lite","android"]},{"id":1867,"title":"YOLOv4 With TensorFlow","description":"YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.0, Android. 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Keras and TensorFlow Keras.\r\n\r\n","tags":["code","keras","tensorflow","library","computer-vision","segmentation","unet","models","model-store"]},{"id":1804,"title":"Text Preprocessing in Python using spaCy library","description":"In this article, we have explored Text Preprocessing in Python using spaCy library in detail. This is the fundamental step to prepare data for applications.","tags":["article","tutorial","spacy","lemmatization","named-entity-recognition","natural-language-processing","part-of-speech-tagging","preprocessing","tokenization"]},{"id":1803,"title":"A Deep Dive into the Wonderful World of Preprocessing in NLP","description":"A glimpse into the surprisingly deep and interesting world of preprocessing in NLP.","tags":["article","tutorial","natural-language-processing","preprocessing","tokenization"]},{"id":1802,"title":"SentencePiece","description":"Unsupervised text tokenizer for Neural Network-based text generation.\r\n\r\n","tags":["code","paper","research","library","machine-translation","natural-language-processing","tokenization","word-segmentation"]},{"id":1801,"title":"Faiss","description":"A library for efficient similarity search and clustering of dense vectors.\r\n","tags":["code","library","clustering","embeddings","similarity-search"]},{"id":1800,"title":"jellyfish","description":"\ud83c\udf90 a python library for doing approximate and phonetic matching of strings.","tags":["code","library","natural-language-processing","text-matching","text-similarity","jellyfish","levenshtein","sounde","x-hamming","metaphon","e-jaro-winkler","fuzzy-search"]},{"id":1799,"title":"FlashText","description":"Extract Keywords from sentence or Replace keywords in sentences.\r\n\r\n","tags":["article","code","paper","research","library","natural-language-processing","regex","text-extraction","arxiv:1711.00046"]},{"id":1798,"title":"Star Clustering","description":"A clustering algorithm that automatically determines the number of clusters and works without hyperparameter fine-tuning.","tags":["code","library","automl","clustering"]},{"id":1797,"title":"Contextualized Topic Models","description":"A python package to run contextualized topic modeling.","tags":["code","paper","research","attention","bert","transformers","library","contextualized-embeddings","embeddings","natural-language-processing","topic-modeling","arxiv:2004.07737"]},{"id":1796,"title":"Top2Vec","description":"Top2Vec learns jointly embedded topic, document and word vectors.\r\n\r\n","tags":["code","library","document-embeddings","embeddings","natural-language-processing","topic-modeling","word-embeddings"]},{"id":1795,"title":"Chakin ","description":"Simple downloader for pre-trained word vectors.","tags":["code","library","embeddings","natural-language-processing","word-embeddings"]},{"id":1794,"title":"Contrastic Learner","description":"A simple to use pytorch wrapper for contrastive self-supervised learning on any neural network.\r\n\r\n","tags":["code","pytorch","contrastive-loss","library","self-supervised-learning"]},{"id":1793,"title":"Keras-FewShotLearning","description":"Some State-of-the-Art few shot learning algorithms in tensorflow 2.","tags":["code","keras","tensorflow","library","few-shot-learning"]},{"id":1792,"title":"Anomaly Detection Toolkit (ADTK)","description":"A Python toolkit for rule-based/unsupervised anomaly detection in time series\r\n\r\n","tags":["code","library","anomaly-detection","time-series","unsupervised-learning"]},{"id":1791,"title":"scikit-fuzzy","description":"scikit-fuzzy is a fuzzy logic toolkit for SciPy.","tags":["code","scikit-learn","library","scipy","fuzzy-logic"]},{"id":1790,"title":"ThunderSVM: A Fast SVM Library on GPUs and CPUs","description":"Exploits GPUs and multi-core CPUs to achieve high efficiency with SVMs.","tags":["code","support-vector-machines","library","gpu"]},{"id":1789,"title":"NGBoost","description":"Natural Gradient Boosting for Probabilistic Prediction\r\n\r\n","tags":["article","code","library","gradient-boosting","natural-gradients","uncertainty-estimation"]},{"id":1788,"title":"LightGBM - 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From the ndarray object to applied linear algebra. Project is 70% done.","tags":["article","code","tutorial","python","numpy"]},{"id":1737,"title":"codeBERT - Automated code docstring review with transformers","description":"codeBERT provide a one command line to check if your code docstrings are up-to-date.\r\n","tags":["code","tutorial","video","huggingface","attention","bert","machine-learning","transformers","library","natural-language-processing","ml-on-code","machine-learning-on-code","documentation"]},{"id":1736,"title":"Matplotlib Style Configurator","description":"Ever wondered what all those matplotlib rc parameters do? Here's a interactive plot style customizer, made with Streamlit.","tags":["code","matplotlib","streamlit","demo"]},{"id":1735,"title":"Criticker Dataset","description":"Yet another dataset about Movies, TV Shows and Games","tags":["article","code","dataset","demo"]},{"id":1734,"title":"Data ANZ Virtual Internship","description":"For the given data of 100 hypothetical customers of their transaction history of 3 months, draw some unique and interesting insights with the features. 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","tags":["code","paper","research","video","sql","natural-language-processing","demo","salesforce","acl-2020","photon"]},{"id":1725,"title":"First Steps with TensorFlow.js","description":"How to create basic AI models and use more sophisticated models with TensorFlow.js.","tags":["article","tutorial","tensorflow","tensorflow-js"]},{"id":1724,"title":"Image Classifier: In the Browser","description":"Using Tensorflow.js to make the prediction directly in the browser.","tags":["article","code","tutorial","tensorflow","tensorflow-js","convolutional-neural-networks","computer-vision","image-classification","demo"]},{"id":1723,"title":"BioSyn","description":"Biomedical Entity Representations with Synonym Marginalization","tags":["code","paper","research","health","library","named-entity-recognition","natural-language-processing","biomedical","demo","entity-normalization","synonym","biosyn","arxiv:2005.00239"]},{"id":1722,"title":"spaczz: Fuzzy matching and more for spaCy","description":"Fuzzy matching and more functionality for spaCy.","tags":["code","spacy","library","natural-language-processing","regex","fuzzy-matching"]},{"id":1721,"title":"TensorflowTTS","description":"Real-Time State-of-the-art Speech Synthesis for Tensorflow 2.","tags":["code","tensorflow","library","natural-language-processing","speech","speech-synthesis","text-to-speech-synthesis"]},{"id":1720,"title":"JAX: Accelerated Machine Learning Research","description":"This talk will introduce JAX and its core function transformations with a live demo. ","tags":["code","tutorial","video","jax","scipy-2020"]},{"id":1719,"title":"Texthero","description":"Text preprocessing, representation and visualization from zero to hero.","tags":["article","code","library","clustering","natural-language-processing","preprocessing","data-cleaning","text-processing","texthero"]},{"id":1718,"title":"Mathematics for Machine Learning - Linear Algebra","description":"Welcome to the \u201cMathematics for Machine Learning: Linear Algebra\u201d course, offered by Imperial College London. ","tags":["tutorial","video","linear-algebra"]},{"id":1717,"title":"TaBERT","description":"Pretraining for Joint Understanding of Textual and Tabular Data","tags":["article","code","paper","research","attention","bert","transformers","library","natural-language-processing","pretraining","tabular-data","acl-2020","tabert","arxiv:2005.08314"]},{"id":1716,"title":"Introduction to Anomaly Detection in Python","description":"Provides an introduction to anomaly detection in the context of machine learning.","tags":["article","code","tutorial","machine-learning"]},{"id":1715,"title":"Image Classifier","description":"Pure JavaScript Image Classifier","tags":["article","machine-learning","computer-vision","image-categorization"]},{"id":1714,"title":"Text Generator","description":"A Text Generator based on Markov Chain","tags":["article","code","hidden-markov-models","machine-learning"]},{"id":1713,"title":"Core Machine Learning Implementations","description":"This repo contains mathematical derivations and python implementations in numpy for key machine learning algorithms","tags":["article","code","tutorial","decision-trees","linear-regression","logistic-regression","neural-networks","random-forests","regression","gradient-boosting","k-nearest-neighbors","decision-tree"]},{"id":1711,"title":"Forecasting the weather with neural ODEs","description":"Interesting blog post applying neural ODEs to the problem of weather forecasting.","tags":["article","forecasting","neural-ode","weather"]},{"id":1710,"title":"NLP-task-visualizer-app","description":"This application designed with streamlit library will help in visualizing NLP tasks on text entered by you. ","tags":["article","code","tutorial","machine-learning","library","natural-language-processing","data-science"]},{"id":1709,"title":"Anti-Patterns in NLP (8 types of NLP idiots)","description":"A talk which discusses the recurring industrial problems in making NLP solutions. ","tags":["tutorial","video","python","attention","bert","deep-learning","transformers","natural-language-processing","search","transfer-learning"]},{"id":1708,"title":"Multithreaded Machine Learning Training & Inference in Browser","description":"How to train and test a deep neural network model in browser by complying to browser standards","tags":["article","code","tensorflow-js","machine-learning"]},{"id":1706,"title":"PokeZoo","description":"A deep learning based web-app developed using the MERN stack and Tensorflow.js. ","tags":["code","javascript","node-js","react","tensorflow","tensorflow-js","deep-learning","machine-learning","full-stack","computer-vision","image-classification","demo"]},{"id":1705,"title":"Handwritten Japanese Character Recognition by Transfer Learning","description":"A Transfer Learning based approach to recognizing the handwritten Hiragana characters of the Kusushiji-MNIST(KMNIST) dataset. ","tags":["code","dataset","paper","research","keras","residual-networks","transfer-learning","arxiv:1512.03385"]},{"id":1704,"title":"LSTM Forecast Model for Stock Price Prediction using Keras","description":" Easy to understand LSTM forecast model for Stock Price Prediction. 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","tags":["paper","research","natural-language-processing","pretraining","paraphrasing","marge","arxiv:2006.15020"]},{"id":1652,"title":"Julia for Pythonistas","description":"Julia looks and feels a lot like Python, only much faster. It's dynamic, expressive, extensible, with batteries included, in particular for Data Science.","tags":["code","notebook","tutorial","julia","python"]},{"id":1651,"title":"High-Resolution Image Inpainting","description":"High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsampling.\r\n","tags":["article","paper","research","computer-vision","inpainting","demo","high-resolution","image-inpainting","arxiv:2005.11742"]},{"id":1650,"title":"The Reformer - Pushing the Limits of Language Modeling","description":"An in-depth understanding of each of the key features of the Reformer.","tags":["article","code","notebook","paper","research","tutorial","huggingface","transformers","language-modeling","natural-language-processing","reformer","arxiv:2001.04451"]},{"id":1649,"title":"Model Serving using FastAPI and streamlit","description":"Simple example of usage of streamlit and FastAPI for ML model serving.","tags":["article","code","tutorial","video","fastapi","computer-vision","semantic-segmentation","streamlit","segmentation","deeplabv3"]},{"id":1648,"title":"STUMPY: A Powerful and Scalable Python Library for Time Series","description":"STUMPY is a powerful and scalable Python library for computing a Matrix Profile, which can be used for a variety of time series data mining tasks.","tags":["article","code","video","library","anomaly-detection","time-series","numba","dask","pattern-matching","matrix-profile","pydata"]},{"id":1647,"title":"Mixed Precision Training ","description":"Mixed precision  Investigation  in using  16-bit and 32-bit floating-point types in a model during training","tags":["article","tutorial","machine-learning","training","mixed-precision"]},{"id":1646,"title":"Twitter Turing Test","description":"Can you guess whether this tweet is written by a human or generated by a neural network?","tags":["code","dataset","tutorial","huggingface","python","gpt2","transformers","natural-language-processing","text-generation","demo","twitter","modelzoo"]},{"id":1645,"title":"Using Data Science Pipelines for Disaster Response","description":"Uses ETL and ML pipeline to build an NLP system for classification of messages into appropriate disaster categories","tags":["article","code","flask","machine-learning","natural-language-processing","data-science","natural-disasters","etl"]},{"id":1644,"title":"A research guide for data scientists","description":"Tips on research from top data scientists","tags":["article","research","deep-learning","natural-language-processing"]},{"id":1643,"title":"FastAPI for Flask Users","description":"A comprehensive guide to FastAPI with a side-by-side code comparison with Flask ","tags":["api","article","tutorial","fastapi","flask"]},{"id":1642,"title":"Sending Email with .docx Attachment using Python","description":"This article will explore how to send email with attachment in Python and how it can be integrated in your existing data science projects.","tags":["article","tutorial","python","program-development"]},{"id":1641,"title":"Hugging Captions","description":"Generate realistic instagram worthy captions using transformers given a hasthtag and a small text snippet.","tags":["article","code","huggingface","transformers","computer-vision","image-captioning","language-modeling","natural-language-processing","text-generation","instagram"]},{"id":1640,"title":"Smooth Adversarial Training","description":"ReLU activation function significantly weakens adversarial training due to its non-smooth nature. Hence we propose smooth adversarial training (SAT).","tags":["paper","research","relu","adversarial-learning","adversarial-training","sat","arxiv:2006.14536"]},{"id":1639,"title":"Computer Vision Recipes","description":"This repository provides examples and best practice guidelines for building computer vision systems.","tags":["article","code","tutorial","pytorch","computer-vision","crowd-counting","image-classification","object-detection","segmentation","action-recognition","recipe","keypoints-detection"]},{"id":1638,"title":"Dakshina Dataset","description":"A collection of text in both Latin and native scripts for 12 South Asian languages.","tags":["code","dataset","paper","research","natural-language-processing","languages","dakshina"]},{"id":1637,"title":"AquVitae: The Easiest Knowledge Distillation Library","description":"AquVitae is a Python library that is the easiest to perform Knowledge Distillation through a very simple API. This library supports TensorFlow and PyTorch. 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Word alignment is applied to produce a synthetic spanisQA corpus.\r\n","tags":["code","paper","research","natural-language-processing","natural-language-understanding","question-answering","spanish","arxiv:1912.05200"]},{"id":1635,"title":"A Weighted Mutual k-Nearest Neighbour for Classification Mining","description":"kNN revisited","tags":["code","paper","research","k-nearest-neighbors","arxiv:2005.08640"]},{"id":1634,"title":"Introduction to Probabilistic Programming","description":"A brief intoduction to Probabilistic Programming, a tool for modelling tasks with uncertainty.","tags":["article","code","tutorial","deep-learning","machine-learning","uncertainty","probability"]},{"id":1633,"title":"FACEAPP Gender Swap Fake detection","description":"In this project we used Deep Learning to detect fake images generated by FaceApp app gender swap feature.","tags":["code","fastai","deep-learning"]},{"id":1630,"title":"Semantic Segmentation + Background Removal + Style Transfer","description":"Running multiple TF Lite models to perform semantic segmentation, remove background, and apply style transfer. ","tags":["code","notebook","tutorial","computer-vision","tf-lite"]},{"id":1629,"title":"TF Lite Semantic Segmentation Models","description":"Faster and lighter TF Lite models can perform semantic segmentation. ","tags":["article","code","tutorial","deep-learning","library","computer-vision","tf-lite"]},{"id":1628,"title":"Deep Reinforcement Learning Amidst Lifelong Non-Stationarity","description":"How can robots learn in changing, open-world environments? We introduce dynamic-parameter MDPs, to capture environments with persistent, unobserved changes. ","tags":["article","paper","research","reinforcement-learning","non-stationarity","off-policy","markov-decision-process","arxiv:2006.10701"]},{"id":1627,"title":"Timecraft: Synthesizing Time Lapse Videos of Paintings","description":"A learning-based method for synthesizing time lapse videos of paintings.","tags":["article","code","paper","research","video","keras","tensorflow","cvpr-2020","paintings","time-lapse","arxiv:2001.01026"]},{"id":1626,"title":"5 Genetic Algorithm Applications Using PyGAD","description":"This tutorial introduces PyGAD, an open-source Python library for implementing the genetic algorithm and training machine learning algorithms.","tags":["article","code","library","genetic-algorithms","pygad"]},{"id":1625,"title":"Beginner's Guide to Altair Visualization","description":"Getting started with Visualization using Altair on Kaggle with this simple tutorial.","tags":["code","notebook","tutorial","visualization","altair","exploratory-data-analysis"]},{"id":1624,"title":"Avatarify - Create Your Own Photorealistic Avatars","description":"Photorealistic avatars for video-conferencing apps. 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","tags":["code","paper","research","tutorial","video","pytorch","computer-vision","image-captioning","cvpr-2020","grounding","visual-grounding","video-object-grounding","object-grounding","arxiv:2003.10606"]},{"id":1398,"title":"Super-resolution Variational Auto-Encoders","description":"VAE with RealNVP prior and Super-Resolution VAE in PyTorch.","tags":["code","paper","research","tutorial","pytorch","autoencoders","variational-autoencoders","computer-vision","super-resolution","unsupervised-learning","respresentation-learning","generative-model","cifar10","arxiv:2006.05218"]},{"id":1393,"title":"RESTAURANT RECOMMENDATION USING YELP DATASET","description":"Built a personalized recommender web app using Yelp dataset of restaurants using Hybrid Matrix factorization model. 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We will keep updating it on an almost daily basis\r\n\r\n","tags":["article","tutorial","deep-learning","machine-learning"]},{"id":1281,"title":"AI Automated dubbing","description":"Build an end-to-end Youtube audio translation platform using AWS serverless architecture","tags":["article","code","tutorial","aws","audio","dubbing"]},{"id":1279,"title":"Perceptron to Deep-Neural-Network: The Road Less Traveled","description":"A journey from Perceptron to Deep Neural Networks in a sequential fashion.","tags":["article","code","notebook","tutorial","neural-networks","multilayer-perceptrons","perceptron"]},{"id":1278,"title":"Automatically Generate Multiple Choice Questions (MCQs) ","description":"Automatically Generate Multiple Choice Questions (MCQs) from any content with BERT Summarizer, Wordnet, and Conceptnet","tags":["article","code","tutorial","attention","bert","transformers","natural-language-processing","question-generation","wordnet","conceptnet"]},{"id":1275,"title":"Exploration Strategies in Deep Reinforcement Learning","description":"Exploitation versus exploration is a critical topic in reinforcement learning. 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","tags":["code","notebook","paper","research","tutorial","finance","differential-machine-learning","risk-management","arxiv:2005.02347"]},{"id":1197,"title":"Scale Invariant Feature Transform For Cirebon Mask Classification","description":"This is about my project in Image Classification focus to Pattern Recognition about Cirebon Mask Classification in MATLAB. ","tags":["code","machine-learning","random-forests","support-vector-machines","pattern-recognition","k-nearest-neighbors","decision-tree","matlab","cirebon-mask"]},{"id":1194,"title":"Divide Hugging Face Transformers Training Time By 2","description":"Reducing training time helps to iterate more in a fixed budget time and thus achieve better results.","tags":["article","code","tutorial","huggingface","transformers","natural-language-processing","optimization","mixed-precision","batch-sizing"]},{"id":1193,"title":"DeepMind x UCL |  Intro to Machine Learning & AI","description":"In this lecture series, research scientists from leading AI research lab, DeepMind, deliver 12 lectures on an exciting selection of topics in Deep Learning.","tags":["course","tutorial","video","machine-learning","deepmind","ucl"]},{"id":1192,"title":"BERT Summarization","description":"This folder contains colab notebooks that guide you through the summarization by BERT and GPT-2 to play with your data.","tags":["code","tutorial","attention","bert","transformers","natural-language-processing","summarization"]},{"id":1191,"title":"Syntactic Search by Example","description":"We present a system that allows a user to search a large linguistically annotated corpus using syntactic patterns over dependency graphs. ","tags":["paper","research","search","allenai","syntactic-patterns","spike","arxiv:2006.03010"]},{"id":1190,"title":"Image Augmentations for GAN Training","description":"We systematically study the effectiveness of various existing augmentation techniques for GAN training in a variety of settings. ","tags":["paper","research","generative-adversarial-networks","data-augmentation","image-augmentation","arxiv:2006.02595"]},{"id":1189,"title":"DogandCatBreed Classifier","description":"Model can to learn to differentiate between these 37 distinct categories","tags":["article","code","fastai","pytorch"]},{"id":1185,"title":"PyTorch Transformers Tutorials","description":"A set of annotated Jupyter notebooks, that give user a template to fine-tune transformers model to downstream NLP tasks such as classification, NER etc. ","tags":["code","tutorial","huggingface","pytorch","transformers","named-entity-recognition","natural-language-processing","question-answering","text-classification","text-summarization","wandb","multi-class","multi-label"]},{"id":1184,"title":"Bank Marketing Campaign","description":"Predict if the client will subscribe to a term deposit based on the analysis of the marketing campaigns the bank performed.","tags":["code","tutorial","library","#marketing#campaign#bank"]},{"id":1182,"title":"A beginner\u2019s guide to understanding the AI buzz words ","description":"A beginner\u2019s guide to understanding the buzz words -AI, ML, NLP, Deep Learning, Computer Vision, and Data Science","tags":["article","tutorial","machine-learning","starter-guide"]},{"id":1181,"title":"TensorflowTTS: Real-Time SOTA Speech Synthesis for Tensorflow 2.0","description":"TensorflowTTS provides real-time state-of-the-art speech synthesis architectures such as Tacotron2, Melgan, FastSpeech.","tags":["code","tensorflow","natural-language-processing","speech","speech-synthesis","text-to-speech"]},{"id":1180,"title":"The Transformer \u2026 \u201cExplained\u201d?","description":"An intuitive explanation of the Transformer by motivating it through the lens of CNNs, RNNs, etc.","tags":["article","tutorial","convolutional-neural-networks","recurrent-neural-networks","transformers","natural-language-processing"]},{"id":1178,"title":"Machine Learning with Fastai","description":"The fastai library is based on research into deep learning best practices undertaken at fast.ai, and includes support for Vision, Text, tabular and Collab","tags":["code","tutorial","python","pytorch","deep-learning","computer-vision"]},{"id":1176,"title":"doc2vec Paragraph Embeddings for Text Classification","description":"Text classification model which uses gensim's Doc2Vec for generating paragraph embeddings and scikit-learn Logistic Regression for classification. ","tags":["code","scikit-learn","document-embeddings","embeddings","natural-language-processing","sentiment-analysis","text-classification","gensim","doc2vec"]},{"id":1173,"title":"Rocopicker","description":"Practice for Classifying  the game character by using CNN\r\n","tags":["article","code","notebook","tutorial","tensorflow","tensorflow-js","convolutional-neural-networks","transfer-learning"]},{"id":1172,"title":"Converting images to TF Records","description":"A Colab Notebook showing how to convert an image dataset (for classification) to TF Records and more.","tags":["code","notebook","tutorial","tensorflow","computer-vision","images","tf-records"]},{"id":1171,"title":"New York Yellow Cab Fares","description":"Created a predictor using classification algorithms to predict the fare of a new york taxi cab.","tags":["code","research","transportation"]},{"id":1169,"title":"Regression Testing","description":"This project leverages machine learning techniques to clean, analyze, and make predictions on any inputted csv dataset.","tags":["code","flask","library","plotly","regression-tests"]},{"id":1168,"title":"Integrated Gradients","description":"This tutorial walks you through an implementation of Integrated Gradients, an ML interpretabilit technique described in Axiomatic Attribution for Deep Networks.","tags":["article","code","notebook","paper","research","interpretability","gradients","integrated-gradients","arxiv:1703.01365"]},{"id":1167,"title":"From Pre-trained Word Embeddings to Pre-trained Language Models","description":"from Static Word Embedding to Dynamic (Contextualized) Word Embedding.","tags":["article","tutorial","attention","bert","transformers","contextualized-embeddings","embeddings","language-modeling","natural-language-processing","word-embeddings","pretraining"]},{"id":1166,"title":"Acme: A Research Framework for Reinforcement Learning","description":"A library of reinforcement learning components and agents.","tags":["code","paper","research","library","reinforcement-learning","acme","deepmind","arxiv:2006.00979"]},{"id":1165,"title":"YOLOv4","description":"A TensorFlow 2.0 implementation of YOLOv4: Optimal Speed and Accuracy of Object Detection.","tags":["code","tutorial","tensorflow","computer-vision","object-detection","yolo","yolo-v4"]},{"id":1162,"title":"Deep Q-Network on Space Invaders. ","description":"This is a PyTorch implementation of a Deep Q-Network agent trained to play the Atari 2600 game of Space Invaders.","tags":["article","code","paper","research","tutorial","pytorch","deep-q-networks","q-learning","reinforcement-learning","arxiv:1312.5602"]},{"id":1159,"title":"Learning To Classify Images Without Labels","description":"A two-step approach where feature learning and clustering are decoupled.","tags":["code","paper","research","tutorial","clustering","computer-vision","image-classification","self-supervised-learning","unsupervised-learning","arxiv:2005.12320"]},{"id":1155,"title":"GaborNet","description":"Modified network architecture that focuses on improving convergence and reducing training complexity.","tags":["code","paper","research","python","pytorch","convolutional-neural-networks","library","computer-vision","arxiv:1904.13204"]},{"id":1154,"title":"Data Scientist: The Dirtiest Job of the 21st Century","description":"I am a data scientist\u2026 \r\nI don\u2019t find my job sexy. \r\nI am 40% a vacuum, another 40% a janitor. \r\nAnd the last 20%\u2026 A fortune-teller. ","tags":["article","tutorial","careers","jobs"]},{"id":1152,"title":"Cardiovascular Disease EDA ( Detailed )","description":"To predict the presence or absence of cardiovascular disease (CVD) using the patient examination results.","tags":["code","research","tutorial","python","health","data-science","exploratory-data-analysis","eda","cardiovascular"]},{"id":1151,"title":"Web Mining and Information theory","description":"Mining the Web and playing with Natural Language processing. Implementing Information retrieval System tasks. Going towards the NLP and Performing Machine Learning algorithms. Through these codes and problems, I have understood the information retrieval process of any search engine. These are very useful problems towards sentiment analysis.","tags":["code","information-extraction","information-retrieval","natural-language-inference","natural-language-processing","natural-language-understanding","web-mining"]},{"id":1150,"title":"Practical Sampling Distribution and Central limit theorem ","description":"Whenever it comes to Statistical Data Analysis or Exploratory Data Analysis, we have to go through the distribution of the data. So while working on the very big datasets mostly we apply our analysis on a sample, not the whole population which may lead to certain variations between the sample analysis and population analysis. This approximation introduces us with the Sampling Distribution","tags":["article","code","notebook","tutorial","library","data-analysis","data-science","sampling-distribution","central-limit-theorem","data-engineers"]},{"id":1148,"title":"A Practical guide to building a conversational chatbot","description":"Building a Chatbot from scratch using Keras and NLTK library for a customer service company","tags":["article","code","tutorial","keras","tensorflow","library","natural-language-processing","conversational-ai"]},{"id":1146,"title":"Physics \u2229 ML","description":"A a virtual hub at the interface of theoretical physics and deep learning.","tags":["research","machine-learning","physics","journal"]},{"id":1145,"title":"Learning Dexterity End-to-End","description":"We trained a human-like robot hand to manipulate physical objects with unprecedented dexterity.","tags":["article","tutorial","reinforcement-learning","robotics","wandb","openai","dexterity"]},{"id":1139,"title":"RoBERTa \u2192 Longformer: Build a \"Long\" Version of Pretrained Models","description":"This notebook replicates the procedure descriped in the Longformer paper to train a Longformer model starting from the RoBERTa checkpoint. ","tags":["code","notebook","paper","research","huggingface","attention","bert","transformers","natural-language-processing","pretraining","roberta","longformer","arxiv:2004.05150"]},{"id":1136,"title":"Integrated Gradients for Interpretability","description":"Integrated Gradients is a technique for attributing a classification model's prediction to its input features. ","tags":["paper","research","tutorial","convolutional-neural-networks","deep-learning","interpretability","gradients","arxiv:1703.01365"]},{"id":1135,"title":"Melanoma Detection with Pytorch","description":"In this video, I show you how you can build a deep learning model to detect melanoma with a very high accuracy.","tags":["code","tutorial","video","pytorch","health","computer-vision","resnet","melanoma"]},{"id":1134,"title":"Artificial Neural Networks Back Then","description":"Development of neural networks over history.","tags":["article","code","notebook","tutorial","neural-networks","multilayer-perceptrons","history","perceptron"]},{"id":1133,"title":"Getting Started with Time Series analysis using Pandas","description":"An introductory guide to get started with the Time Series datasets in Python","tags":["article","code","tutorial","time-series","pandas","kaggle"]},{"id":1132,"title":"T5 for Sentiment Span Extraction","description":"Exploring how T5 works and applying it for sentiment span extraction.","tags":["code","notebook","tutorial","video","transformers","natural-language-processing","sentiment-analysis","t5"]},{"id":1131,"title":"Sized Fill-in-the-blank or Multi Mask filling with RoBERTa","description":"Sized fill-in-the-blank or conditional text filling is the idea of filling missing words of a sentence with the most probable choice of words.","tags":["article","code","tutorial","huggingface","attention","bert","transformers","language-modeling","natural-language-processing","slot-filling","roberta","mask-filling","multi-mask-filling"]},{"id":1130,"title":"Generate True or False questions from any content","description":"Automatically generate \u201cTrue or False\u201d questions like the ones you see in school textbooks using  OpenAI GPT2, Sentence BERT, and Berkley parser","tags":["article","code","research","tutorial","attention","bert","gpt2","transformers","constituency-parsing","natural-language-processing","question-generation","text-generation"]},{"id":1128,"title":"Arabic Movie Genres Multi-Label Classifier","description":"Using CNNs to predict the genres of Arabic movies based on their posters.","tags":["code","convolutional-neural-networks","deep-learning","multi-label"]},{"id":1127,"title":"Spam Mail Classifier","description":"Spam Mail Classifier based on Apache Spam Assassin dataset and part of Enron dataset Using ML and DL\r\n","tags":["code","fastai","deep-learning","machine-learning"]},{"id":1126,"title":"Face Recognition","description":"Face Recognition using face_recognition library which used dlib models to identify faces and generate encoding for each face, then using ML model to classify f ","tags":["code","dlib","python","machine-learning"]},{"id":1125,"title":"Money Ball","description":"Applying data wrangling and exploratory data analysis to baseball data. 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","tags":["article","tutorial","deep-learning","knowledge-distillation","model-compression"]},{"id":1117,"title":"Knowledge Distillation with Keras","description":"Discusses the basics of Knowledge Distillation for Neural Nets. ","tags":["article","code","tutorial","deep-learning","knowledge-distillation","model-compression"]},{"id":1115,"title":"Softbot design with WANNS","description":"Soft robots are robots built from highly compliant materials, similar to those found in living organisms. This project explored CPPNs and WANNs to design them","tags":["article","code","research","python","reinforcement-learning","numpy","neuroevolution","alife"]},{"id":1114,"title":"Reinforcement Learning in JAX","description":"Implementation of interesting Deep Reinforcement Learning Algorithms using JAX based libraries (flax, haiku and rlax) As of now tasks come from OpenAI gym","tags":["code","tutorial","jax","reinforcement-learning"]},{"id":1113,"title":"VAE Explorer","description":"A tool that can be used to explore pre-trained tensorflow VAE models and interpolate between data points. 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The hackathon was organized by Analytics Vidhya.","tags":["code","research","fastai","pytorch","computer-vision","image-classification"]},{"id":1105,"title":"torchdata","description":"Equip PyTorch's Dataset with map, cache etc. (like tf.data)","tags":["code","dataset","pytorch","tensorflow","library","data","cache","map","filter","tensorflow-data"]},{"id":1104,"title":"Convnet Galaxy Morphology Classifier","description":"Classify galaxies from Hubble Tuning Fork using Convnet. ","tags":["code","research","tutorial","tensorflow","convolutional-neural-networks","deep-learning","astronomy","computer-vision","image-classification"]},{"id":1100,"title":"Movie Recommendation System","description":"This is a web app which recommends movies based on their plots found on IMDb.","tags":["code","tutorial","flask","python","natural-language-processing","recommendation-systems","heroku"]},{"id":1099,"title":"Supervised Contrastive Learning","description":"Implements the ideas presented in Supervised Contrastive Learning (https://arxiv.org/pdf/2004.11362v1.pdf) by Khosla et al. ","tags":["article","code","paper","research","tutorial","tensorflow","deep-learning","representation-learning","contrastive-learning","supervised-contrastive-learning","arxiv:2004.11362"]},{"id":1095,"title":"Bitcoin Prediction","description":"Predict bitcoin values using Seq2Seq","tags":["code","research","bitcoin-prediction"]},{"id":1094,"title":"Paraphrase Any Question with T5  (Text-To-Text  Transformer)","description":"Given a question, generate paraphrased versions of the question with T5 transformer. Pretrained model and training script provided.","tags":["article","code","research","tutorial","huggingface","pytorch","transformers","natural-language-processing","question-generation","text-generation","t5","paraphrasing"]},{"id":1093,"title":"MedicalZoo PyTorch","description":"A pytorch-based deep learning framework for multi-modal 2D/3D medical image segmentation","tags":["article","code","notebook","research","tutorial","pytorch","deep-learning","machine-learning","library","computer-vision","segmentation","medical-image-segmentation","volumetric-segmentation","medical-image-proccessing","reproducible-medical-imaging"]},{"id":1086,"title":"House Prices: Advanced Regression Techniques","description":"This project aims to predict the House Prices in Boston, given various features describing the house.","tags":["code","decision-trees","multinomial-regression","random-forests","regression","support-vector-machines","decision-tree","ada-boost"]},{"id":1085,"title":"Book on Convex Optimization | Cambridge University Press ","description":"This book by Boyd and Vandenberghe. Copyright in this book is held by Cambridge University Press, who have kindly agreed to keep the book available on web.","tags":["article","research","convex-optimization","cambridge"]},{"id":1084,"title":"Beginner\u2019s guide to Machine Learning Model Deployment","description":"Are you a beginner in the field of machine learning and wondering how to bring your project to live. I'm was in the same situation when I started learning ML. M","tags":["api","article","code","tutorial","flask","machine-learning","production","deployment","beginner"]},{"id":1083,"title":"Reverse Image Search","description":"Have you ever wondered how google image search works or How amazon can retrieve products similar to the image that we upload in the app/site? To achieve this ta","tags":["article","code","deep-learning","machine-learning","computer-vision","image-categorization"]},{"id":1082,"title":"Ideal Restaurant Spot Finder","description":"To find ideal spots in the city where food retail stores can be put up, aiming at the demographic, thereby helping the owners of the outlets to earn profits.","tags":["code","notebook","paper","research","matplotlib","machine-learning","clustering","geospatial","data-science","geopy","chloropleth","folium","one-hot-encoding"]},{"id":1081,"title":"AI Basketball Analysis","description":"\ud83c\udfc0   AI web app and API to analyze basketball shots and shooting pose. ","tags":["api","code","tutorial","convolutional-neural-networks","sports","computer-vision","pose-estimation","application","r-cnn","coco","basketball","shot-analysis","pose-analysis","open-pose","faster-r-cnn"]},{"id":1080,"title":"Dash DETR Detection App","description":"A User Interface for DETR built with Dash. 100% Python.","tags":["code","tutorial","computer-vision","object-detection","segmentation","interactive","plotly","dash","panoptic-segmentation","detr","end-to-end"]},{"id":1079,"title":"Book on Optimization Models and Applications","description":"Freely available book on optimisation methods mainly about constrained optimisation problems","tags":["article","tutorial","optimization","constrained-optimization"]},{"id":1078,"title":"Building Footprint Extraction","description":"The project retrieves satellite imagery from Google and performs building footprint extraction using a U-Net. ","tags":["code","tutorial","keras","tensorflow","deep-learning","computer-vision","gis","remote-sensing"]},{"id":1076,"title":"Adversial Auto Encoder (PyTorch)","description":"Adversial autoencoder, to generate mnist digit.","tags":["code","tutorial","pytorch","autoencoders","adversarial-learning","mnist"]},{"id":1075,"title":"Zero-Shot Learning for Text Classification","description":"A visual summary of \u201cTrain Once, Test Anywhere\u201d paper for zero-shot text classification","tags":["article","tutorial","natural-language-processing","zero-shot-learning"]},{"id":1072,"title":"Novice-AI Music Co-Creation via AI-Steering Tools","description":"Collaborative Convolutional Counterpoint","tags":["code","paper","research","library","music","audio","music-generation"]},{"id":1071,"title":"Music Source Separation in the Waveform Domain","description":"We provide an implementation of Demucs and Conv-Tasnet for music source separation on the MusDB dataset. They can separate drums, bass and vocals from the rest ","tags":["code","paper","research","music"]},{"id":1070,"title":"AI Debate Master","description":"Created and deployed a bot made to debate with a human on any\r\ngiven topic. Employed a Doc2Vec model using Gensim library in Python","tags":["code","tutorial","natural-language-processing","sentiment-analysis","conversational-ai","doc2vec"]},{"id":1069,"title":"NLP News Category","description":"The objective of this repository is to create a NLP bot for when you give the robot the headline of the news and a short description it will return the genre.","tags":["code","tutorial","machine-learning","natural-language-processing"]},{"id":1068,"title":"Effects of News Sentiments on Stock Predictions","description":"Project is based on the Natural Language Processing technique called Sentiment Analysis. 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Wrote a notebook to explain SIR model and based the pandemic on same.","tags":["code","covid-19","eda"]},{"id":1064,"title":"One Shot Art","description":"Implemented one shot learning approach to classify paintings according to artists.","tags":["code","tutorial","art","few-shot-learning","one-shot-learning"]},{"id":1059,"title":"Wheat Detection \ud83c\udf3e","description":"This is a project for detecting and creating bounding box of wheat heads \ud83c\udf3e.","tags":["article","code","deep-learning","machine-learning","computer-vision","object-detection"]},{"id":1058,"title":"SmartFeed.ai","description":"NLP Based Article Recommendation System ","tags":["code","flask","python","scikit-learn","latent-dirichlet-allocation","library","natural-language-processing","recommendation-systems"]},{"id":1056,"title":"Font Recognition Using Deep Learning - DeepFont ( Adobe )","description":"DeepFont Paper is a technique created by Adobe.Inc to detect font from images using deep learning . 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Providing real time experience using many interfaces like web, command line and notebooks.","tags":["code","tutorial","library","natural-language-processing"]},{"id":1027,"title":"Don't Touch Your Face!","description":"Using machine learning to detect when you touch your face, to keep yourself and your surroundings safe of coronavirus.","tags":["code","tutorial","tensorflow-js","transfer-learning","covid19"]},{"id":1026,"title":"Lyrics-Based Music Genre Classifier","description":"Classify the genre (Rock, Pop, Hip-Hop, Not Available, Metal, Other, Country, Jazz, Electronic, R&B, Indie, Folk) of the song by its lyrics.","tags":["code","tutorial","deep-learning","machine-learning","natural-language-processing","text-classification"]},{"id":1025,"title":"Forex Prediction","description":"Using neural networks to predict movement of forex direction.","tags":["code","tutorial","deep-learning","machine-learning","finance","natural-language-processing","forex"]},{"id":1024,"title":"Sentiment Classification for UtaPass & KKBOX Reviews","description":"Text classification for reviews of UtaPass & KKBOX using different deep learning models.","tags":["code","tutorial","deep-learning","machine-learning","natural-language-processing","text-classification"]},{"id":1023,"title":"Gradient Descent  Algorithm","description":"This is a linear regression Machine Learning model based on the \"chirps dataset\" which has 'X' variable as number of chirps by a cricket and 'Y' as temperature.","tags":["article","code","linear-regression","regression","gradient-boosting"]},{"id":1022,"title":"The designer - gpt2 bot that talks about UX Design","description":"This twitter profile spits out thoughts on design and development. 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","tags":["article","code","tutorial","aws","tensorflow","attention","bert","transformers","training","natural-language-processing","sagemaker","albert"]},{"id":1008,"title":"face mask detection ","description":"detects whether a person wearing a mask or not","tags":["code","research","tutorial","video","css","flask","html","python","tensorflow","deep-learning","library","computer-vision","transfer-learning"]},{"id":1007,"title":"Humour.ai : Language Model that can crack Jokes","description":"A Language model that can make you laugh. Humour.ai model tries to\r\ncomplete a sentence in a humourous way given some input words. ","tags":["code","huggingface","gpt2","transformers","language-modeling","natural-language-processing"]},{"id":1005,"title":"Traffic Sign Detection","description":"Detecting traffic sign images using GSTRB dataset.","tags":["code","tutorial","python","scikit-learn","open-cv","template-matching"]},{"id":1004,"title":"Why initialization is important? ","description":"A small blog post explaining the importance of weight initialization in Neural Networks ","tags":["article","tutorial","deep-learning","machine-learning","weight-initialization","eigen-value","eigen-vectors"]},{"id":1003,"title":"Exploratory Data Analysis using stack overflow data","description":"Data Visualization and simple EDA to produce insights for business insights using powerful graphing libraries like plotly.","tags":["article","code","research","data-science","exploratory-data-analysis"]},{"id":1002,"title":"yolov5,yolov3,yolov4 -> tensorRT","description":"The project is the encapsulation of nvidia official yolo-tensorrt implementation. And you must have the trained yolo model(.weights) and .cfg file from the dark","tags":["code","library","tensorrt","yolov4","jetson-nano","yolo-v3","darknet","ubuntu","yolov4-tiny","yolov3-tiny","win10","l4t","jetson-nx","yolov5"]},{"id":1001,"title":"TabNet : Attentive Interpretable Tabular Learning","description":"PyTorch implementation of TabNet paper.","tags":["article","code","paper","research","pytorch","attention","library","interpretability","tabular-data","tabnet","arxiv:1908.07442"]},{"id":1000,"title":"CMU LTI Low Resource NLP Bootcamp 2020","description":"A low-resource natural language and speech processing bootcamp held by the Carnegie Mellon University Language Technologies Institute in May 2020.","tags":["code","course","tutorial","video","natural-language-processing","low-resource"]},{"id":999,"title":"MediaPipe","description":"Simplest way for researchers and developers to build world-class ML solutions and applications for mobile, edge, cloud and the web. ","tags":["code","library","3d","computer-vision","face-detection","object-detection","segmentation","hand-tracking","mediapipe"]},{"id":998,"title":"ML Cheatsheets","description":"Set of illustrated Deep Learning cheatsheets covering the content of the CS 230 class","tags":["tutorial","machine-learning","guides","cheatsheets"]},{"id":997,"title":"AutoSweep: Recovering 3D Editable Objects from a Single Photo","description":"Fully automatic framework for extracting editable 3D objects directly from a single photograph.","tags":["article","paper","research","3d","computer-vision","autosweep","object-generation","arxiv:2005.13312"]},{"id":994,"title":"Large SVDs - Dask + CuPy + Zarr + Genomics","description":"Using Dask to perform Singular Value Decomposition on large datasets","tags":["article","code","video","genomics","cupy","dask","singular-value-decomposition","zarr"]},{"id":993,"title":"DETR: End-to-End Object Detection with Transformers","description":"A new method that views object detection as a direct set prediction problem. ","tags":["article","code","notebook","paper","research","tutorial","transformers","computer-vision","natural-language-processing","object-detection","segmentation","panoptic-segmentation","arxiv:2005.12872"]},{"id":992,"title":"Identifying the Number of Open Ion Channels with HMMs","description":"A write-up on how we almost won the \u201cUniversity of Liverpool \u2014 Ion Switching\u201d Kaggle competition using Hidden Markov Models.","tags":["article","tutorial","hidden-markov-models","biology","kaggle","ion-channels"]},{"id":991,"title":"NLP Viewer \ud83e\udd17","description":"A simple website for browsing popular NLP datasets.","tags":["huggingface","library","natural-language-processing","streamlit","datasets"]},{"id":990,"title":"Solving Optimization Problems with JAX","description":"JAX can be used to solve a range of simple to complex optimization problems with matrix methods.","tags":["article","notebook","paper","research","tutorial","jax","xla","autograd","optimization"]},{"id":989,"title":"NLP for Developers: Shrinking Transformers | Rasa","description":"In this video, Rasa Senior Developer Advocate Rachael will talk about different approaches to make transformer models smaller.","tags":["tutorial","video","transformers","model-compression","natural-language-processing","pruning","quantization","distillation"]},{"id":987,"title":"Zero To One For NLP","description":"A collection of all resources for learning NLP","tags":["article","tutorial","deep-learning","natural-language-processing","natural-language-understanding"]},{"id":986,"title":"Neural Topological SLAM for Visual Navigation","description":"Topological representations for space that effectively leverage semantics and afford approximate geometric reasoning.","tags":["article","paper","research","video","computer-vision","robotics","visual-navigation","cvpr-2020","slam","topology"]},{"id":985,"title":"Translate RegEx in Natural Language Using Deep Learning","description":"A complete tutorial and code on how to build a model able to translate RegEx into natural language.","tags":["article","code","notebook","tutorial","deep-learning","library","regex","ml-on-code","machine-learning-on-code","regular-expression"]},{"id":984,"title":"Tree-hugger","description":"Tree-hugger is a Python library to automate code mining. It is meant to be language-agnostic, extendable and high-level.","tags":["article","code","library","ml-on-code","code-mining","machine-learning-on-code"]},{"id":983,"title":"Telecom delinquency model","description":"Created 6 deliquency models based on logistic regression, SVM, KNN, Naive bayes,Decision tree and Random forest models and reported the results.","tags":["code","python","scikit-learn","machine-learning","classification","model-selection","hyperparameter-optimization"]},{"id":982,"title":"How to write Web apps using simple Python for Data Scientists?","description":"So, are we doomed to learn web frameworks? Or to call our developer friend for silly doubts in the middle of the night?\r\nThis is where StreamLit comes in and del","tags":["article","code","tutorial","flask","web-design","streamlit","data-science"]},{"id":981,"title":"Applications of MCMC for Cryptography and Optimization","description":"This post is about understanding MCMC Methods with the help of some Computer Science problems.","tags":["article","code","tutorial","stochastic-optimization","datascience","mcmc"]},{"id":980,"title":"Super-BPD for Fast Image Segmentation","description":"We propose direction-based super-BPD, an alternative to superpixel, for fast generic image segmentation, achieving state-of-the-art real-time result.","tags":["code","paper","research","tutorial","computer-vision","segmentation","cvpr-2020","super-pixel","super-pizel"]},{"id":979,"title":"Taxi Demand Prediction NewYorkCity","description":"Predict the number of pickups as accurately as possible for each region in a 10 -min interval.","tags":["article","code","notebook","tutorial","python","time-series","time-series-forecasting"]},{"id":978,"title":"TensorFlow.js - Gesture Controlled 2048","description":"Gesture Controlled 2048 built with TensorFlow.js","tags":["code","tutorial","tensorflow","tensorflow-js","video-games","computer-vision","gesture-recognition"]},{"id":975,"title":"Self-Supervised Learning -- UC Berkeley Spring 2020","description":"Lecture on self-supervised learning from CS294-158-SP20: Deep Unsupervised Learning.","tags":["code","course","notebook","video","self-supervised-learning","unsupervised-learning","berkeley"]},{"id":974,"title":"PixelLib","description":"Pixellib is a library for performing segmentation of images. ","tags":["code","library","computer-vision","semantic-segmentation","segmentation","instance-segmentation"]},{"id":972,"title":"Next Word Prediction","description":"Using transformers to predict next word and predict <mask> word.","tags":["code","tutorial","transformers","language-modeling","natural-language-processing"]},{"id":970,"title":"Job Classification","description":"Job Classification done using Techniques of NLP and ML.\r\n\r\nDataset used from Kaggle of Indeeed job posting.","tags":["code","natural-language-processing","supervised-learning"]},{"id":968,"title":"Self Supervised Representation Learning in NLP","description":"An overview of self-supervised pretext tasks in Natural Language Processing","tags":["article","tutorial","natural-language-processing","representation-learning","self-supervised-learning"]},{"id":966,"title":"Building an Intelligent Twitter Bot","description":"The volume of information going through Twitter per day makes it one of the best platforms to get information on any subject of interest. ","tags":["article","code","notebook","paper","research","tutorial","natural-language-processing","text-classification"]},{"id":965,"title":"GANs in Computer Vision : An article review series ","description":"An article series where we review the most important research papers on GANs from 2015 to today.  6 articles,  20 papers, 20000 words","tags":["article","tutorial","deep-learning","generative-adversarial-networks","computer-vision","generation","unsupervised-learning"]},{"id":964,"title":"Neural Network Intelligence (NNI)","description":"NNI is a lightweight but powerful toolkit to help users automate Feature Engineering, Neural Architecture Search, Hyperparameter Tuning and Model Compression.","tags":["code","library","automl","feature-engineering","model-compression","hyperparameter-optimization","neural-architecture-search","auto-tuning"]},{"id":963,"title":"Build your first data warehouse with Airflow on GCP","description":"What are the steps in building a data warehouse? What cloud technology should you use? How to use Airflow to orchestrate your pipeline?","tags":["article","code","tutorial","production","airflow","google-cloud-platforms","data-warehouse"]},{"id":962,"title":"Basic ML Algorithms from scratch.","description":"Implement basic Machine Learning Algorithms from scratch in python.","tags":["article","code","tutorial","python","machine-learning","library","natural-language-processing"]},{"id":961,"title":"How to Build Robust Embeddings for Visual Similarity Tasks","description":"This repository I package a bunch of tips and tricks to efficiently train deep learning models in computer vision","tags":["article","code","pytorch","deep-learning","computer-vision","embeddings","metric-learning","triplet-loss"]},{"id":960,"title":"Neural ODE Explained","description":"Explains \"Neural Ordinary Differential Equations\", a very interesting idea came out in NIPS 2018.","tags":["article","code","tutorial","recurrent-neural-networks","differential-equation","neural-ode","ordinary-differential-equations"]},{"id":959,"title":"An Introduction to Bayes' Theorem","description":"This blog post introduces the reader to one of the most important concept in probability theory -  Bayes' Theorem","tags":["article","tutorial","bayesian-deep-learning","probabaility-and-statistics"]},{"id":958,"title":"Machine Learning for Facies Classification of North Sea Well Logs","description":"The dataset is provided by GEOLINK in the Google Drive Public geoscience Data","tags":["code","machine-learning"]},{"id":957,"title":"Colbert AI","description":"Colbert AI is a Deep Learning Language Model that generates text in the style of Stephen Colbert's famous monologues.","tags":["article","code","huggingface","transformers","language-modeling","natural-language-processing"]},{"id":956,"title":"FIFA-19 analysis and prediction","description":"Data visualisation and prediction of the overall score of a player using various linear regression algorithms, ensembling algorithms, and a neural network.","tags":["code","tutorial","feed-forward-neural-networks","regression","feature-selection"]},{"id":955,"title":"Open Geoscience Computing Repository","description":"Open geoscience computing of open geoscience datasets available in open databases from Google Drive, SEG Wiki, and US DoE Geothermal Data Repository OpenEi","tags":["code","research","machine-learning","energy","geology","oil"]},{"id":954,"title":"How to Train Your Neural Net","description":"Deep learning for various tasks in the domains of Computer Vision, Natural Language Processing, Time Series Forecasting using PyTorch 1.0+.","tags":["article","code","research","tutorial","python","pytorch","deep-learning","classification","computer-vision","domain-adaptation","image-classification","model-compression","named-entity-recognition","natural-language-processing","pruning","text-classification","time-series"]},{"id":953,"title":"Cross-entropy for Classification","description":"Usages of cross-entropy for binary classification, multi-class classification, and multi-label classification.","tags":["article","tutorial","classification","cross-entropy","multi-class","multi-label"]},{"id":952,"title":"Face Mask Detector","description":"A simple Streamlit frontend for face mask detection in images using a pre-trained Keras CNN model + OpenCV and model interpretability.","tags":["code","tutorial","keras","convolutional-neural-networks","computer-vision","interpretability","object-detection","opencv","streamlit","face-mask"]},{"id":951,"title":"YoloV3 implementation in keras and tensorflow 2.2","description":"YoloV3 Real Time Object Detector in tensorflow 2.2.","tags":["code","tutorial","keras","tensorflow","computer-vision","object-detection","yolo","yolo-v3"]},{"id":950,"title":"Sound Event Detection in Synthetic Domestic Environment","description":"We present a comparative analysis of the performance of state-of-the-art sound event detection system and study the robustness to noise and signal degradation. ","tags":["paper","research","audio","audio-classification","audio-tagging","sound"]},{"id":949,"title":"Generative Adversarial Networks for Outlier Detection ","description":"PyTorch implementation of a GAN architecture for the problem of outlier detection.","tags":["article","code","paper","research","pytorch","generative-adversarial-networks","anomaly-detection","outlier-detection","arxiv:1809.10816"]},{"id":948,"title":"Plant Disease Detection","description":"This website help you to detect disease in your plant\ud83c\udf33 based to the plant's leaf\ud83c\udf43 image","tags":["article","code","machine-learning","environment","computer-vision","streamlit"]},{"id":947,"title":"Self Driving Car","description":"This project is a demonstration of a working model of self driving car \ud83d\ude97\ud83d\ude97 identifying and following lanes using powerful computer vision \ud83d\udd76\ud83d\udd76 algorithms.","tags":["article","code","autonomous-vehicles","computer-vision"]},{"id":946,"title":"Replicating Airbnb's Amenity Detection (documentary series)","description":"Airbnb's engineering team shared an article on how they used computer vision to detection amenities in photos. It read like a recipe so I replicated it.","tags":["article","code","tutorial","video","business","project-management","computer-vision","detectron2"]},{"id":945,"title":"Migrating from OS.PATH to PATHLIB Module in Python","description":"Learn how to use the modern pathlib module to perform tasks you have been using os.path for.","tags":["article","tutorial","python","program-development"]},{"id":944,"title":"Math Symbols Explained with Python","description":"Learn the meaning behind mathematical symbols used in Machine Learning using your knowledge of Python.","tags":["article","tutorial","python"]},{"id":943,"title":"Transfer Learning in NLP with Tensorflow Hub and Keras","description":"Learn how to integrate and finetune tensorflow-hub modules in Tensorflow 2.0","tags":["article","tutorial","keras","tensorflow","natural-language-processing","transfer-learning","tf-hub"]},{"id":942,"title":"Keras: The Next Five Years by Fran\u0107ois Chollet","description":"Keras: the next five years. 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","tags":["article","code","tutorial","pytorch","tensorflow","computer-vision","object-detection","pretraining","models","tf-hub","bit"]},{"id":940,"title":"TAO: A Large-Scale Benchmark for Tracking Any Object","description":"A diverse dataset for Tracking Any Object (TAO) consisting of 2,907 high resolution videos, captured in diverse environments, which are half a minute long on ","tags":["article","dataset","paper","research","video","computer-vision","object-tracking","benchmark","tao","video-tracking","arxiv:2005.10356"]},{"id":939,"title":"Time Series Classification Using Deep Learning","description":"In this article, I will introduce you to a new package called timeseries for fastai2 that I lately developed. ","tags":["article","code","tutorial","fastai","library","time-series"]},{"id":938,"title":"Content and Style Disentanglement for Artistic Style Transfer","description":"Hi-Res style transfer and interpolation between styles","tags":["code","paper","research","video","deep-learning","computer-vision","style-transfer"]},{"id":937,"title":"In Model Extraction, Don\u2019t Just Ask \u2018How?\u2019: Ask \u2018Why?\u2019","description":"Designing an effective extraction attack requires that one first settle on a few critical details\u2014the adversary\u2019s goal, capabilities, and knowledge.","tags":["article","paper","research","tutorial","adversarial-learning","adversarial-attacks","model-extraction","arxiv:2003.04884"]},{"id":936,"title":"Kaggle Datasets","description":"Find and use datasets or complete tasks.","tags":["library","datasets","kaggle"]},{"id":935,"title":"Look inside the workings of \"Label Smoothing\"","description":"This blog post describes how and why does \"trick\" of label smoothing improves the model accuracy and when should we use it ","tags":["article","paper","research","tutorial","deep-learning","classification","computer-vision","image-classification","regularization","label-smoothing","arxiv:1906.02629","arxiv:1512.00567"]},{"id":934,"title":"\ud83d\udcc8 Automated Time Series Forecasting","description":"This data app uses Facebook's open-source Prophet library to automatically forecast values into the future. ","tags":["code","tutorial","forecasting","time-series","time-series-forecasting","streamlit"]},{"id":933,"title":"d2l-pytorch","description":"Reproduces the book Dive Into Deep Learning (www.d2l.ai), adapting the code from MXNet into PyTorch.","tags":["code","tutorial","pytorch","book","d2l-ai"]},{"id":932,"title":"SymJAX","description":"A symbolic CPU/GPU/TPU programming","tags":["code","jax","library","xla","autograd","symjax","symbolic-programming"]},{"id":931,"title":"Bayesian Active Learning (BaaL)","description":"BaaL is an active learning library by ElementAI. 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","tags":["article","tutorial","deep-learning","library","multilayer-perceptrons"]},{"id":884,"title":"Build a Textual Similarity Web App with TensorFlow.js","description":"Have you wondered how search engines understand your queries and retrieve relevant results? 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","tags":["article","research","attention","deep-learning","self-attention","transformers","language-modeling","natural-language-processing","natural-language-understanding"]},{"id":868,"title":"RXNMapper: Unsupervised Attention-Guided Atom-Mapping","description":"The atom-mapping information was learned by an ALBERT model trained in an unsupervised fashion on a large dataset of chemical reactions.","tags":["article","code","paper","research","attention","bert","transformers","natural-language-processing","unsupervised-learning","chemistry","albert","molecules","atom-mapping","rxnmapper","chemical-compounds"]},{"id":867,"title":"Multiple Linear Regression in python","description":"It is used to explain the relationship between independent variables and a single continuous dependent variable.","tags":["article","code","tutorial","linear-regression","multinomial-regression","regression"]},{"id":866,"title":"HuggingFace nlp library","description":"nlp is a lightweight and extensible library to easily share and load dataset and evaluation metrics, already providing access to ~100 datasets and ~10 evaluatio","tags":["code","notebook","huggingface","library","metrics","natural-language-processing","datasets"]},{"id":865,"title":"Simple Linear Regression","description":"This will cover Simple Liner Regression and its  Algorithm and the techniques that is used in along with the formula. I will also cover little bit of math.","tags":["article","code","tutorial","linear-regression","regression"]},{"id":864,"title":"Twitter Sentiment Analysis","description":"This project is based on Natural Language processing (NLP), in this we do sentiment analysis(i.e, how much it is positive or negative) of tweets of any account.","tags":["code","notebook","tutorial","machine-learning","natural-language-processing","sentiment-analysis"]},{"id":863,"title":"S2IGAN: Speech-to-Image Generation via Adversarial Learning","description":"A speech-to-image generation (S2IG) framework is proposed which translates speech descriptions to photo-realistic images without using any text information.","tags":["article","paper","research","computer-vision","image-generation","arxiv:2005.06968"]},{"id":862,"title":"DQN In Pytorch Livestream Series","description":"I'm doing a series of streams about reinforcement learning (starting from Q learning) focused on showing the work in as much detail as possible (e.g. debugging)","tags":["code","tutorial","video","pytorch","deep-q-networks","reinforcement-learning","atari","breakout","cartpole"]},{"id":861,"title":"Flowtron","description":"Auto-regressive flow-based generative network for text to speech synthesis.","tags":["code","paper","research","library","computer-vision","natural-language-processing","speech","speech-synthesis","style-transfer","tacotron","flowtron","iaf","text-to-speech","arxiv:2005.05957"]},{"id":860,"title":"Electra","description":"ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators","tags":["code","research","generative-adversarial-networks","natural-language-processing","electra","text-encoder"]},{"id":859,"title":"An Approach for Table Scraping With Azure Form Recognizer","description":"Invoice handling is the most important task in many different fields that deal with large amounts of data in many different formats. 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","tags":["code","research","fastai","pytorch","deep-learning","computer-vision","image-classification","transfer-learning"]},{"id":851,"title":"Pleural Effusion Detection","description":"Attempting a Convolutional Neural Network to detect pleural effusion in frontal chest radiographs.","tags":["code","research","keras","tensorflow","convolutional-neural-networks"]},{"id":850,"title":"Transformers from Scratch","description":"Attempt to explain directly how modern transformers work, and why, without some of the historical baggage.","tags":["article","code","tutorial","transformers","natural-language-processing","from-scratch"]},{"id":849,"title":"Top Down Introduction to BERT with HuggingFace and PyTorch","description":"I will also provide some intuition into how BERT works with a top down approach (applications to algorithm).","tags":["article","tutorial","huggingface","pytorch","attention","bert","transformers","natural-language-processing","top-down"]},{"id":848,"title":"Using Custom spaCy Components in Rasa","description":"Get a custom spaCy model working inside of Rasa on your local machine.","tags":["article","tutorial","spacy","rasa"]},{"id":847,"title":"HuggingTweets","description":"Tweet Generation with Huggingface.","tags":["code","tutorial","huggingface","transformers","natural-language-processing","text-generation","wandb"]},{"id":846,"title":"Guitar Chords Recognition","description":"An application that predicts the chords when Mel spectrograms of guitar sound are fed into a CNN.","tags":["code","video","convolutional-neural-networks","library","audio","audio-classification","streamlit","chords"]},{"id":845,"title":"Creating and deploying static websites using Markdown and Pelican","description":"A series of articles consisting of a detailed step by step tutorial on how to create and host your personal static website using only Markdown and Python","tags":["article","code","tutorial","python","analytics","pelican","markdown","website","disqus"]},{"id":844,"title":"Little Ball of Fur","description":"Little Ball of Fur is a graph sampling extension library for NetworkX.","tags":["code","library","graph-classification","graph-clustering","graphs","node-classification","community-detection","network-science","network-sampling"]},{"id":843,"title":"T5 fine-tuning","description":"A colab notebook to showcase how to fine-tune T5 model on various NLP tasks (especially non text-2-text tasks with text-2-text approach)","tags":["code","notebook","paper","research","tutorial","transformers","natural-language-processing","t5","text-2-text","arxiv:1910.10683"]},{"id":842,"title":"Identifying Brain Tumor from MRI images using FastAI -DynamicUnet","description":"To use FASTAI unet learner to identify tumours from MRI of Brain, logging loss metrics in Neptune AI logger and compare the results after hyperparameter tuning.","tags":["article","code","fastai","pytorch","deep-learning","computer-vision","segmentation","neptune-ai"]},{"id":841,"title":"BLEURT: Learning Robust Metrics for Text Generation","description":"A metric for Natural Language Generation based on transfer learning.","tags":["code","paper","research","tutorial","metrics","natural-language-processing","text-generation","transfer-learning","language-generation","bleu","sentence-bleu","bertscore","arxiv:2004.04696"]},{"id":840,"title":"Machine Learning on Graphs: A Model and Comprehensive Taxonomy","description":"We propose a simple framework (GraphEDM) and a comprehensive Taxonomy to review and unify several graph representation learning methods.","tags":["paper","research","autoencoders","graph-convolutional-networks","graph-neural-networks","graphs","representation-learning","survey","graph-regularization","arxiv:2005.03675"]},{"id":839,"title":"Underrated ML","description":"A podcast that pitches underrated ideas in Machine Learning.","tags":["podcast","tutorial","machine-learning","underrated-topics"]},{"id":838,"title":"mljar-supervised","description":"\u2728 Automated Machine Learning Python package designed to save time for a data scientist \ud83d\ude0e","tags":["code","linear-regression","regression","library","automl","supervised-learning","multi-class-classification"]},{"id":837,"title":"torchdyn","description":"A PyTorch based library for all things neural differential equations","tags":["code","pytorch","library","neural-differential-equations","torchdyn"]},{"id":836,"title":"Epipolar Transformers","description":"Differentiable \"epipolar transformer\", which enables the 2D detector to leverage 3D-aware features to improve 2D pose estimation.","tags":["code","paper","research","tutorial","video","transformers","3d","computer-vision","natural-language-processing","pose-estimation","epipoplar","cvpr-2020","arxiv:2005.04551"]},{"id":835,"title":"Toward Better Storylines with Sentence-Level Language 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Sergey Levine. 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In order to help investors decide on which mutual fund to pick for an investment.","tags":["code","notebook","tutorial","scikit-learn","random-forests","finance","pandas","numpy","gradient-boosting","decision-tree","xgboost","mutual-funds","multiclass-classification","light-gbm","cat-boost","voting-classifier","ovr-classifier"]},{"id":720,"title":"Sentiment Prediction of Google Play Store Reviews with Tensorflow","description":"How to train your own algorithm to classify the sentiment of the reviews of your app from scratch.","tags":["article","tutorial","keras","tensorflow"]},{"id":719,"title":"Interactive Machine Learning Experiments","description":"\ud83e\udd16 Interactive Machine Learning experiments: \ud83c\udfcb\ufe0fmodels training + \ud83c\udfa8models demo","tags":["article","code","tutorial","javascript","python","tensorflow","tensorflow-js","artificial-general-intelligence","machine-learning"]},{"id":718,"title":"Movie Recommendation Engine","description":"Content-Based Recommendations: Content-Based Recommendation algorithm takes into account the likes and dislikes of the user and generates a User Profile.","tags":["article","tutorial","machine-learning","recommendation-systems","data-science"]},{"id":717,"title":"Synthesizer: Rethinking Self-Attention in Transformer Models","description":"The dot product self-attention is known to be central and indispensable to state-of-the-art Transformer models. 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Libraries make their life simpler. I have come across five cool Python libraries while working ","tags":["article","tutorial","python","machine-learning","library","natural-language-processing","data-science"]},{"id":694,"title":"BLINK: Better entity LINKing","description":"Entity Linking python library that uses Wikipedia as the target knowledge base.","tags":["code","paper","research","library","named-entity-recognition","natural-language-processing","wikification","arxiv:1911.03814"]},{"id":693,"title":"SciTLDR: Extreme Summarization of Scientific Documents","description":"A new automatic summarization task with high source compression requiring expert background knowledge and complex language understanding.","tags":["code","paper","research","tutorial","transformers","natural-language-processing","text-summarization","bart","arxiv:2004.15011"]},{"id":692,"title":"Hands on One-Shot Learning","description":"One-shot learning can be seen as an attempt to create an approach to train machines with a similar ability to learn like humans. 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","tags":["code","paper","research","tutorial","deep-learning","embeddings","graph-classification","graph-clustering","graph-embedding","graphs","node-classification","representation-learning","node-embedding","network-embedding","graph-convolution","network-representation"]},{"id":42,"title":"Text Associated Deep Walk","description":"An implementation of \"Network Representation Learning with Rich Text Information\" (IJCAI '15).","tags":["code","paper","research","tutorial","deep-learning","embeddings","graph-classification","graph-clustering","graph-embedding","graphs","node-classification","representation-learning","node-embedding","network-embedding","graph-convolution","network-representation"]},{"id":41,"title":"DANMF","description":"A sparsity aware implementation of \"Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection\" (CIKM 2018).","tags":["code","paper","research","tutorial","autoencoders","deep-learning","embeddings","graph-classification","graph-clustering","graph-embedding","graphs","node-classification","representation-learning","node-embedding","network-embedding","graph-convolution","network-representation"]},{"id":40,"title":"Binarized Attributed Network Embedding","description":"A sparsity aware implementation of \"Binarized Attributed Network Embedding\" (ICDM 2018).","tags":["code","paper","research","tutorial","deep-learning","embeddings","graph-classification","graph-clustering","graph-embedding","graphs","node-classification","representation-learning","node-embedding","network-embedding","graph-convolution","network-representation"]},{"id":39,"title":"GraRep","description":"A SciPy implementation of \"GraRep: Learning Graph Representations with Global Structural Information\" (WWW 2015). 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","tags":["code","paper","research","tutorial","pytorch","deep-learning","embeddings","graph-classification","graph-clustering","graph-embedding","graphs","node-classification","representation-learning","node-embedding","network-embedding","graph-convolution","network-representation","arxiv:1905.07953"]},{"id":36,"title":" FSCNMF","description":"An implementation of \"Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information Networks\". ","tags":["code","paper","research","tutorial","deep-learning","embeddings","graph-classification","graph-clustering","graph-embedding","graphs","node-classification","representation-learning","node-embedding","network-embedding","graph-convolution","network-representation","arxiv:1804.05313"]},{"id":35,"title":"Boosted Factorization","description":"An implementation of \"Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation\" (ASONAM 2019). ","tags":["code","paper","research","tutorial","deep-learning","embeddings","graph-classification","graph-clustering","graph-embedding","graphs","node-classification","representation-learning","node-embedding","network-embedding","graph-convolution","network-representation","arxiv:1808.08627"]},{"id":34,"title":"Graph2Vec","description":"A parallel implementation of \"graph2vec: Learning Distributed Representations of Graphs\" (MLGWorkshop 2017). ","tags":["code","paper","research","tutorial","deep-learning","embeddings","graph-classification","graph-clustering","graph-embedding","graphs","node-classification","representation-learning","node-embedding","network-embedding","graph-convolution","network-representation","arxiv:1707.05005"]},{"id":33,"title":"M-NMF","description":"A TensorFlow implementation of \"Community Preserving Network Embedding\" (AAAI 2017) ","tags":["code","research","tutorial","tensorflow","deep-learning","embeddings","graph-classification","graph-clustering","graph-embedding","graphs","node-classification","representation-learning","node-embedding","network-embedding","graph-convolution","network-representation"]},{"id":32,"title":"Walklets","description":"A lightweight implementation of Walklets from \"Don't Walk Skip! Online Learning of Multi-scale Network Embeddings\" (ASONAM 2017). ","tags":["code","paper","research","tutorial","deep-learning","embeddings","graph-classification","graph-clustering","graph-embedding","graphs","node-classification","representation-learning","node-embedding","network-embedding","graph-convolution","network-representation","arxiv:1605.02115"]},{"id":31,"title":"GraphWave","description":"A scalable implementation of \"Learning Structural Node Embeddings Via Diffusion Wavelets (KDD 2018)\". ","tags":["code","paper","research","tutorial","deep-learning","embeddings","graph-classification","graph-clustering","graph-embedding","graphs","node-classification","representation-learning","node-embedding","structural-embedding","graph-convolution","network-representation","arxiv:1710.10321"]},{"id":30,"title":"Label Propagation","description":"A NetworkX implementation of Label Propagation from a \"Near Linear Time Algorithm to Detect Community Structures in Large-Scale Networks\" (Physical Review E 200","tags":["code","paper","research","tutorial","graph-classification","graph-clustering","graphs","node-classification","community-detection","node-embedding","network-embedding","arxiv:0709.2938"]},{"id":29,"title":"Signed Graph Convolutional Network","description":"A PyTorch implementation of \"Signed Graph Convolutional Network\" (ICDM 2018). ","tags":["code","paper","research","tutorial","pytorch","deep-learning","graph-convolutional-networks","embeddings","graph-embedding","graph-neural-networks","graphs","node-classification","representation-learning","network-embedding","network-visualization","signed-graphs","link-prediction","arxiv:1808.06354"]},{"id":28,"title":"Attributed Social Network Embedding","description":"A sparsity aware and memory efficient implementation of \"Attributed Social Network Embedding\" (TKDE 2018). ","tags":["code","paper","research","tutorial","deep-learning","graph-convolutional-networks","embeddings","feature-engineering","graph-clustering","graph-embedding","graph-neural-networks","graphs","representation-learning","attributed-embedding","arxiv:1705.04969"]},{"id":27,"title":"APPNP and PPNP","description":"A PyTorch implementation of \"Predict then Propagate: Graph Neural Networks meet Personalized PageRank\" (ICLR 2019). 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