Each category lists general online resources followed by links to GitHub repositories. Hold "Ctrl/Command" while clicking on a link to open it in a new window.

Check Data_notes - Lists of publicly available datasets for machine learning

• Cheatsheets
• Machine learning
  • ML Books
  • ML Courses & Tutorials
  • ML Videos
  • ML Papers
  • ML Tools
  • ML Misc
• Deep Learning
  • DL Books
  • DL Courses & Tutorials
  • DL Videos
  • DL Papers
    • DL Papers Genomics
  • DL Tools
    • DL models
    • DL projects
  • DL Misc

• 101 Machine Learning Algorithms for Data Science with Cheat Sheets - Brief description and R/Python examples of algorithms, categorized into several categories: classification, regression, neural networks, anomaly detection, dimensionality reduction, ensemble learning, clusterint, association rule analysis, regularization

• Machine Learning Cheatsheet - Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more.

• Data Science Cheatsheet - Data Science and ML Cheat Sheet, by Maverick Lin. Source

• cheatsheets-ai - Essential Cheat Sheets for deep learning and machine learning researchers

• machine-learning-cheat-sheet - 30-page MachineLearning cheat sheet with classical equations & diagrams, Tweet by Kirk Borne

• ml_cheatsheet - A 5-pages only Machine Learning cheatsheet focusing on the most popular algorithms under the hood. Online version

• stanford-cs-229-machine-learning - VIP cheatsheets for Stanford's CS 229 Machine Learning. Online version

• awesome-machine-learning - A curated list of awesome Machine Learning frameworks, libraries and software

• awesome-machine-learning-interpretability - A curated list of awesome machine learning interpretability resources

• awesome-machine-learning-operations - A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

• awesome-courses - List of awesome university courses for learning Computer Science

• data-science - "Path to a free self-taught education in Data Science!" - Open Source Society University, a collection of free online courses in logical order of learning data science. Massive list of courses, from linear algebra and calculus to R/Python programming/machine learning

• machine_learning - Machine learning in R notes by Dave Tang

• 100+ Free Data Science Books

• ciml - book "A Course in Machine Learning". Online version

• Interpretable Machine Learning book by Christoph Molnar, A Guide for Making Black Box Models Explainable. LearnPub

• Introduction to Machine Learning book by Nils Nilsson, free PDF

• hands-on-machine-learning-with-r - Hands-on Machine Learning with R: An applied book covering the fundamentals of machine learning with R. Supplementary material, Online version

• mit-deep-learning-book-pdf - MIT Deep Learning Book, PDF of the original http://www.deeplearningbook.org/ book.

• ML_for_Hackers - Code accompanying the book "Machine Learning for Hackers"

• rtemis - Advanced Machine Learning and Visualization in R. Book

• Feature Engineering and Selection: A Practical Approach for Predictive Models by Kuhn and Johnson, GitHub

• 40+ Modern Tutorials Covering All Aspects of Machine Learning, Tweet

• 100-Days-Of-ML-Code - 100 Days of Machine Learning Coding as proposed by Siraj Raval. Illustrated step-by-step guides with code and data. Links to videos.

• Code for Workshop: Introduction to Machine Learning with R by Shirin Glander. More in her blog posts, twitter etc.

• aml-london-2019 - Course materials for Applied Machine Learning course in 2019 in London, by Max Kuhn

• aml-training - The most recent version of the Applied Machine Learning notes, related to the parsnip R package by Max Kuhn

• cs-video-courses - List of Computer Science courses with video lectures

• Data-Analysis-and-Machine-Learning-Projects - Randy Olson's data analysis and machine learning projects

• google-interview-university - List of ML/CS courses. A complete daily plan for studying to become a Google software engineer

• H2O-3 - The third version of H2OAI - Open Source Fast Scalable Machine Learning Platform For Smarter Applications: Deep Learning, Gradient Boosting & XGBoost, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

• machine-learning-for-software-engineers - A complete daily plan for studying to become a machine learning engineer

• Machine-Learning-in-R - Workshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles

• LatinR-2019-h2o-tutorial - H2O Machine Learning Tutorial in R

• lecture_i2ml - Introduction to Machine Learning (regression/classification, performance evaluation, parameter tuning, random forests), Python

• mlcourse_open - OpenDataScience Machine Learning course (Both in English and Russian). Python-based ML course, with video lectures. Video

• mlcourse.ai - Open Machine Learning course mlcourse.ai, 2018 English version. Online version, Video

• MTH594_MachineLearning - The materials for the course MTH 594 Advanced data mining: theory and applications (Dmitry Efimov, American University of Sharjah)

• pattern_classification - A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks

• sklearn-classification - Data Science Notebook on a Classification Task, using sklearn and Tensorflow. Jupyter Notebook, the Census Income Dataset to predict whether an individual's income exceeds $50K/yr based on census data. Docker-wrapped

• supervised-ML-case-studies-course - Supervised machine learning case studies in R. Book

• useR-machine-learning-tutorial - useR! 2016 Tutorial: Machine Learning Algorithmic Deep Dive. IPython notebooks running R kernel

• 10 Powerful YouTube Channels for Data Science Aspirants - Analytics Vidhya's post. Sentdex, 3Blue1Brown, freeCodeCamp.org, StatQuest, Krish Naik, Python Programmer, Corey Schafer, Tech With Tim, Brandon Foltz, 365 Data Science

• NC ASA Webinar: Introduction to Machine Learning, by Dr. Funda Gunes, Part 1, Part 2. A one hour overview of the main machine learning concepts

• Learning from data - Statistical learning theory course from Caltech, taught by Feynman Prize winner Professor Yaser Abu-Mostafa. Videos, slides

• Statistical Machine Learning: Spring 2017 by Ryan Tibshirani, Larry Wasserman, Carnegie Mellon University.

• Domingos, Pedro. “A Few Useful Things to Know about Machine Learning.” Communications of the ACM 55, no. 10 (October 1, 2012): 78. https://doi.org/10.1145/2347736.2347755. Twelve lessons for machine learning. Overview of machine learning problems and algorithms, problem of overfitting, causes and solutions, curse of dimensionality, issues with high-dimensional data, feature engineering, bagging, boosting, stacking, model sparsity. Video lectures

• mlr3 - Machine learning in R R package, the unified interface to classification, regression, survival analysis, and other machine learning tasks. GitHub repo, mlr3gallery - Examples of problems and code solutions, mlr3 Manual - mlr3 bookdown. More on the mlr3 package site, including videos

• The Algorithms - R - GitHub repo with code examples of main machine learning algorithms

• algorithms_in_ipython_notebooks - A repository with IPython notebooks of algorithms implemented in Python. [https://github.com/rasbt/algorithms_in_ipython_notebooks]

• awesome-decision-tree-papers - A collection of research papers on decision, classification and regression trees with implementations

• lares - R Library for Analytics and Machine Learning

• ml_techniques - R code for performing typical ML tasks and techniques, e.g., naive Bayes, random forest, by Shirin Glander

• ML-From-Scratch - Bare bones Python implementations of some of the fundamental Machine Learning models and algorithms

• Machine- and deep learning notes by Ming Tang

• Gradient Boosting Essentials in R Using XGBOOST

• MLPB - Machine Learning Problem Bible, problems and solutions in R. XGBoost, SVM, neural networks, and other methods

• Awesome Deep Learning - A curated list of awesome Deep Learning tutorials, projects and communities

• Awesome - Most Cited Deep Learning Papers - the most cited deep learning papers

• AI and DeepRL - source code, links and other learning materials related to Artificial Intelligence, especially focused on Deep Reinforcement Learning

• THE NEURAL NETWORK ZOO - infographics of different neural network architectures, explanation of each, references to the original papers

• Over 150 of the Best Machine Learning, NLP, and Python Tutorials, Tweet by Andrew Trask

• Deep learning with R by François Chollet (the creator of Keras) with J. J. Allaire (the founder of RStudio and the author of the R interfaces to Keras and TensorFlow), R notebooks, Python notebooks

• The Deep Learning textbook by Ian Goodfellow, Yoshua Bengio and Aaron Courville. Includes lectures in .key and .pdf formats, videos discussing different chapters. https://www.deeplearningbook.org/

• Fundamentals-of-Deep-Learning-Book - Python code companion to the O'Reilly "Fundamentals of Deep Learning" book

• Dive into Deep Learning - An interactive deep learning book with code, math, and discussions, based on MXNet, useful as general learning material. https://d2l.ai/

• Grokking-Deep-Learning - Python code for the "Grokking Deep Learning" book by Andrew Trask

• neural-networks-and-deep-learning - Code samples for "Neural Networks and Deep Learning" book. Python/Theano examples, theory, and practice of deep learning by Michael Nielsen

• python-machine-learning-book - "Python Machine Learning (3rd Ed.) Code Repository" book by Sebastian Raschka, iPython notebooks

• reinforcement-learning-an-introduction - Python code for Sutton & Barto's book "Reinforcement Learning: An Introduction (2nd Edition)"

• The Matrix Calculus You Need For Deep Learning paper by Terence Parr and Jeremy Howard

• Deep Learning with Keras and TensorFlow in R Workflow by Brad Boehmke. GutHub repo with Rmd files for data download, code examples, lectures.

• MIT 6.874 Computational Systems Biology: Deep Learning in the Life Sciences - machine/deep learning, genomics, systems biology MIT course, Spring 2020. Taught by David Gifford, Manolis Kellis, Sachit Dinesh Saksena, Corban Swain, Timothy Fei Truong Jr. Lecture videos, slides, reading references. GitHub repo

• Colah's blog, articles on neural networks, visualization - Illustrated and highly informative posts on types of neural networks and their applications by Christopher Olah

• Introduction to Deep Learning course, D2L, Berkeley STAT 157, Jupyter notebooks, GitHub repository with slides and notebooks, Video course

  • d2l.ai - Dive into Deep Learning: An interactive deep learning book with code, math, and discussions, based on the NumPy interface, Jupyter notebooks
  • Mathematics for Deep Learning, d2l.ai - systematic deep learning math, linear algebra and matrix operations, eigendecomposition, single- and multivariable calculus, integral calculus, maximum likelihood and optimization, statistics (random variables, distributions, naive Bayes), information theory

• Practical Deep Learning for Coders, v3 - FAST.AI main course. Introduction to Machine Learning for Coders - another course by Jeremy Howard, with videos

• Step-by-step guides to learn Applied Machine Learning - Machine Learning Mastery web site aggregating structured posts for beginner and intermediate machine learning users, deep learning

• Stanford Computer Science courses CS221/229/230 ― Several GitBook-formatted courses on Artificial Intelligence, machine learnint, deep learning

• Machine Learning courses by Thorsten Joachims - Thorsten Joachims' home page with links to courses and more. CS4780/CS5780 Machine Learning for Intelligent Systems, CS6780 Advanced Machine Learning, and more. Videos and slides

• Machine and deep learning courses by Google - a collection of Google Developers courses

• DeepLearningProject - An in-depth machine learning tutorial introducing readers to a whole machine learning pipeline from scratch, by Spandan Madan,Visual Computing Group, Harvard University. Python

• DL_CSHSE_spring2018 - Deep learning, Anton Osokin, Higher School of Economics, Computer Sciences Department (Russian), course material, and video lectures

• dlaicourse - Deep learning course, TensorFlow, Jupyter notebooks, by Laurence Moroney, Google

• easy-tensorflow - Simple and comprehensive tutorials in TensorFlow, by Jahandar Jahanipour. Online version

• homemade-machine-learning - Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained, by Oleksii Trekhleb. Medium blog post

• image_classification_keras_tf - Workshop material for Image Classification & Natural Language Processing with Python, Keras and TensorFlow, by Shirin Glander

• keras-workshop - Keras R workshop, by Doug Ashton. slides, simple examples

• nn-from-scratch - Implementing a Neural Network from Scratch – An Introduction, by Denny Britz. Notes

• Practical_DL - Deep learning course, Python notebooks, PDF lectures, videos. DL course co-developed by YSDA, HSE and Skoltech

• stat453-deep-learning-ss20 - Intro to Deep Learning, UW-Madison (Spring 2020) by Sebastian Raschka, videos

• stat479-machine-learning-fs19 - Course material for STAT 479: Machine Learning (FS 2019) taught by Sebastian Raschka at University Wisconsin-Madison, pdf slides

• stat479-deep-learning-ss19 - Course material for STAT 479: Deep Learning (SS 2019) taught by Sebastian Raschka at University Wisconsin-Madison, pdf slides

• Tensorflow-101 - Tensorflow Tutorials using Jupyter Notebook with data

• TensorFlow-Course - Simple and ready-to-use tutorials for TensorFlow. Step-by-step instructions with screenshots. By Amirsina Torfi

• TensorFlow-Examples - TensorFlow Tutorial and Examples for Beginners with Latest APIs, by Aymeric Damien

• TensorFlow-LiveLessons - "Deep Learning with TensorFlow" LiveLessons, Jupyter notebooks, by Jon Krohn

• pytorch-tutorial - PyTorch Tutorial for Deep Learning Researchers. Basic, Intermediate, and Advanced code examples, by Yunjey Choi

• MIT Introduction to Deep Learning | 6.S191 - MIT video course by Alexander Amini, Ava Soleimani, and guests. Dense and informative ~45min lectures covering various topics of deep learning. introtodeeplearning.com - course web site with slides, video, and other material. GitHub

• Deep Learning Crash Course for Beginner - a 1h 25m overview of deep learning techniques, highly informative narrative by Jason Dsouza

• Series of eight video lectures on the math of machine learning by Tinnam Ganesh. "Elements of Neural Networks & Deep Learning", Part1,2,3, Parts 4,5, Parts 6,7,8

• Coursera Neural Networks for Machine Learning — Geoffrey Hinton - Video course of short lectures introducing theoretical foundations of machine learning

• Introduction to Deep Learning course, D2L, Berkeley STAT 157, video lectures by Alex Smola. Accompanies the https://d2l.ai/ book

• Introduction to Deep Learning - Deep Learning basics with Python, TensorFlow and Keras. Several posts, each ncludes video, text and code tutorial

• Machine Learning & Deep Learning Fundamentals, by DeepLizard - information-dense short videos about fundamentals and math behind neural networks. Blog posts

• Brandon Rohrer's YouTube channel - short videos about basics of deep learning and neural networks

• Undergraduate machine learning at UBC 2012 by Nando de Freitas. Slides

• Deep learning at Oxford 2015 by Nando de Freitas. Slides

• TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners - 7 hours of walk-through programming with Tim Ruscica. Links to Google Colaboratory Notebooks are in the description

• Undergraduate machine learning at UBC 2012 by Nando de Freitas. Slides

• Deep learning at Oxford 2015 by Nando de Freitas. Slides

• Heroes of Deep Learning, Interviews by Andrew Ng.

• Advanced Deep Learning & Reinforcement Learning - a video-course on deep RL taught at @UCL by DeepMind researchers

• Weights & Biases video and code tutorials - Short videos and text with Python code for individual topics, by Lukas Biewald. GitHub repo with code. Weights & Biases Youtube channel

• Machine Learning Foundations - Machine Learning Foundations is a free training course where you’ll learn the fundamentals of building machine learned models using TensorFlow with Laurence Moroney. Computer vision-focused

• UCL Course on Reinforcement Learning by David Silver. Slides and video lectures

• Deep Learning Papers Reading Roadmap - Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!

• Papers with code - Systematic collection of machine- and deep learning papers with code

• Deep_learning_examples - Examples of using deep learning in Bioinformatics. Deep Learning in Bioinformatics

• deep_learning_papers - A place to collect papers that are related to deep learning and computational biology, by Harold Pimentel

• Deep-Learning-Papers-Reading-Roadmap - Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!

• deeplearning-biology - A list of papers on deep learning implementations in biology

• Machine-learning-for-proteins - List of papers about machine learning for proteins

• Key Papers in Deep Reinforcement Learning, Twitter

• LeCun, Bengio, and Hinton, “Deep Learning.” - Classical deep learning review. Areas of application, historical development, principles of supervised learning, stochastic gradient descent (Figure 1 - illustration of forward and backpropagation, with equations), convolutional neural networks for image recognition and in other areas, language processing, recurrent neural networks, LSTMs

• Vincent et al., “Extracting and Composing Robust Features with Denoising Autoencoders.” - Denoising autoencoder paper, statistical formulations

• Schmidhuber, “Deep Learning in Neural Networks.” - Deep overview of deep learning history. Year-by-year description of types of DL, approaches, algorithmic (backpropagation) improvements, problems, and solutions

• Deep Review: Opportunities and obstacles for deep learning in biology and medicine - A collaboratively written review paper on deep learning, genomics, and precision medicine led by Casey Greene and many others

• Deep Learning Genomics Primer - This tutorial is a supplement to the manuscript, A Primer on Deep Learning in Genomics (Nature Genetics, 2018) by James Zou, Mikael Huss, Abubakar Abid, Pejman Mohammadi, Ali Torkamani & Amalio Telentil. Box 1 and 2 - concepts and definitions. Box 3 - online resources (cloud platforms, GPU services, software libraries, educational resources, more). Python tutorial on detecting DNA motifs.

• Eraslan et al., “Deep Learning.” - Deep learning in genomics review. Big data description, evolution of machine learning into deep learning with the help of GPUs. Supervised learning - Four major classes of neural networks (fully connected, convolutional, recurrent and graph convolutional). Two unsupervised learning techniques, autoencoders and generative adversarial networks (GANs). From basic logistic regression to each network architecture illustrated on figures, theory descriptions, examples of applications in genomics. Transfer learning, model zoos, interpretation/feature importance.

• Angermueller et al., “Deep Learning for Computational Biology.” - Review on machine learning, (epi)genomics examples. Supervised vs. unsupervised learning. Deep neural networks. Box 1 - network basics. Box 2 - convolutional NN. TOOLS: Caffe, Theano, Torch7, TensorFlow. Data preparation, model training and optimization

• Min, Lee, and Yoon, “Deep Learning in Bioinformatics.” - Deep neural networks in bioinformatics. Overview of deep learning development, programming libraries, basic structure of neural networks, convolutional NNs, recurrent NNs. Table 4 - Omics applications, biomedical imaging, biomedical signal processing. References. Code examples (Jupyter notebooks) of eight bioinformatics deep learning applications

• Zou et al., “A Primer on Deep Learning in Genomics.” - Deep learning in genomics overview (feed-forward, convolutional, recurrent) and a Python tutorial on detecting DNA motifs. Box 1 and 2 - concepts and definitions. Box 3 - online resources (cloud platforms, GPU services, software libraries, educational resources, more). GitHub repo and Colab notebook with Interactive tutorial to build a convolutional neural network to discover DNA-binding motifs

• Pérez-Enciso, and Zingaretti. “A Guide for Using Deep Learning for Complex Trait Genomic Prediction.” Genes, 2019 - Deep learning for predicting phenotypes from genomics data. Deep learning basics, definitions

• Sakellaropoulos, Theodore, Konstantinos Vougas, Sonali Narang, Filippos Koinis, Athanassios Kotsinas, Alexander Polyzos, Tyler J. Moss, et al. “A Deep Learning Framework for Predicting Response to Therapy in Cancer.” Cell Reports, December 2019 - Drug response prediction from gene expression data. Deep Neural Network (DNN, H2O.ai framework) compared with Elastic Net, Random Forest. Trained on highly variable (by MAD) gene expression in 1001 cell lines and 251 drugs pharmacogenomic dataset (GDSC. CCLP) to predict IC50. Hyper-parameter optimization using 5-fold cross-validation and minimizing Mean Square Error. Batch correction between the datasets Tested on unseen patient cohorts (OCCAMS, MD Anderson, TCGA, Multiple Myeloma Consortium) to predict IC50 and test low, medium, high IC50 groups for survival differences. RDS files data, R code

• M. Jannesari, M. Habibzadeh, H. Aboulkheyr, P. Khosravi, O. Elemento, M. Totonchi, and I. Hajirasouliha. “Breast Cancer Histopathological Image Classification: A Deep Learning Approach.” In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2018 - Breast cancer image classification. Data from Stanford Tissue Microarray Database (TMAD) and Breast Cancer Histopathological Database (BreakHis), >6K images. Different variants of ResNet and Inception architectures. Data augmentation (resizing, rotation, cropping, flipping). Training details. Classification into malignant and benign, or into subtypes. Can handle images at different magnifications. ResNet performs better. GitHub repository includes crawler to get images

• Interactive_Tools - Interactive Tools for Machine Learning, Deep Learning and Math. Play with deep neural network in browser

• keras - Deep Learning for humans http://keras.io/

• MXNet-Gluon-Style-Transfer - neural artistic style transfer using MXNet. PyTorch and Torch implementations available

• pathology_learning - Using traditional machine learning and deep learning methods to predict stuff from TCGA pathology slides

• ruta - Unsupervised Deep Architechtures in R, autoencoders. Requires Keras and TensorFlow. Book

• tensor2tensor - Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research

• Janggu - deep learning interface to genomic data (FASTA, BAM, BigWig, BED, GFF). Numpy-like Bioseq and Cover objects accessable by Keras. Includes model evaluation and interpretation features. Pypi, Docs, Janggu - Deep learning for genomics

• maui - Multi-omics Autoencoder Integration. Latent factors from different data types (stacked variational autoencoders), and their clustering, testing for association with survival. Tested vs. latent factors extracted using Multifactor Analysis (MFA) and iCluster+, on TCGA colorectal cancer RNA-seq, SNPs, CNVs. Evaluation of Colorectal Cancer Subtypes and Cell Lines Using Deep Learning

• Ludwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code. GitHub

• Mask_RCNN - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

• PennAI - AI-Driven Data Science, entry-level machine learning interface for non-experts. A System for Accessible Artificial Intelligence

• TPOT - A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. Simplified interface to many machine learning algorithms. Scaling Tree-Based Automated Machine Learning to Biomedical Big Data with a Feature Set Selector

• PyTorch model zoos

• Keras model zoos

• Tensorflow model zoos

• Kipoi - a model zoo for genomics. Examples of transfer learning, predicting pathogenic variants, TFBSs. Avsec et al., “The Kipoi Repository Accelerates Community Exchange and Reuse of Predictive Models for Genomics.”, GitHub repo

• Caffe Model Zoo

• Project DeepSpeech - A TensorFlow implementation of Baidu's DeepSpeech architecture. Transcribe audio data, English model available. Documentation

• Deeplearning-digital-pathology - Python code demonstrating image classification using Keras with Caffe or TensorFlow backend, image manipulation utilities

• Weights & Biases Gallery of Curated machine learning reports - selected examples with code

• Machine learning lessons and teaching projects designed for engineers - GitHub repo by Lukas Biewald, the founder of Weights and Biases. Code and video tutorials

• Weights & Biases GitHub examples of deep learning projects

• neuralart_tensorflow - Implementation of "A Neural Algorithm of Artistic Style" by Tensorflow

• Jukebox - music generation neural network. Hierarchical Vector Quantised-Variational AutoEncoder (VQ-VAE) architecture, three separate temporal resolutions. Able to generate singing from lyrics, extend music examples. Dhariwal et al., “Jukebox: A Generative Model for Music.”, Blog post with examples of generated music

• Selfie2Anime online tool and a GitHub repo

• Deep learning resources - (cloud) platforms, software, educational resources. From Zou et al., “A Primer on Deep Learning in Genomics.”

• Collections of GitHub repositories of deep learning projects, Analytics Vidhya

• Google Colab Free GPU Tutorial

• How to use R with Google Colaboratory?, direct link to a new R-notebook

• Deep-Reinforcement-Learning-Algorithms-with-PyTorch - PyTorch implementations of deep reinforcement learning algorithms and environments

• ML Visuals - Visuals contains figures and templates which you can reuse and customize to improve your scientific writing. Google Slides