Computer Science References for researchers

This repository is made to gather different resources for researchers and students in Computer Science specially for Machine Learning, Deep Learning, Reinforcement Learning and Quantum Computing. Additional usefull resources can be find at the end, tutorials and others github pages about librairies in AI.

Table of Contents

• Textbooks
  • Artificial intelligence
  • Machine Learning
  • Deep Learning
  • Reinfrocement Learning
  • Meta-Learning
  • Meta-Reasoning
  • Lifelong or Continual Learning
  • Quantum Computing
  • Mathematics for Machine Learning
  • Causality
  • Computational Neuroscience
  • Data Analyse and Data Science
  • Additional books
• Articles
  • Artificial intelligence
  • Machine Learning
  • Deep Learning
  • Knowledge Graph / Knowledge Graph Net
  • Reinforcement Learning
  • Meta-Learning
  • Meta-Reasoning
  • Consciousness
  • Federated Learning
  • Lifelong or Continual Learning
  • Explainability, interpretability
  • Quantum Computing
  • Causality
  • Computational Neuroscience
• Posts and thesis
  • Blog posts
  • Thesis
• Scientific Research
  • Research Guide
  • Research Papers & Academic Resources
  • NLP
  • List of most cited researchers
  • People to follow
  • AI Communities
• Resources
  • Libraries
    • PyTorch
      • Notebooks
    • Tensorflow
      • Notebooks
    • Packages
    • Notebooks
  • Quantum Computing
  • Blogs
• Datasets
• Best Practices
• Explainability, interpretability
• Courses
  • MOOCs
  • YouTube
  • Support Courses
  • Notebooks
  • Sites
• General and technical additional books
  • General
  • Technical
• Contribution

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Textbooks

Artificial Intelligence

• Fundamental Algorithms: 1 (Artificial Intelligence for Humans), Jeff Heaton, 2013 or the github repository of the author : link
• Artificial Intelligence with Python: A Comprehensive Guide to Building Intelligent Apps for Python Beginners and Developers, Prateek Joshi, 2017
• Artificial Intelligence: A Modern Approach, 4th edition, Stuart Russell & Peter Norvig, 2020

Machine Learning

• The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition, Trevor Hastie & Robert Tibshirani & Jerome Friedman, corrected 2017
• Pattern Recognition and Machine Learning, Christopher M. Bishop, 2011
• Notebooks for each chapter, ctgk
• Programming Collective Intelligence: Building Smart Web 2.0 Applications, Toby Segaran, 2011
• Machine Learning: a Probabilistic Perspective, Kevin P. Murphy, 2012
• Introduction to machine learning with Python: A Guide for Data Scientists, Andreas C. Müller & Sarah Guido, 2016
• Machine Learning, Tom M Mitchell, 2017
• An Introduction to Statistical Learning, 8th printing, Gareth James et al., 2017
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems - 2nd edition, Aurélien Géron, 2019

• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems - 1rst edition, Aurélien Géron, 2019

• Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 - 3rd Edition, Sebastian Raschka & Vahid Mirjalili, 2019

• The Hundred-Page Machine Learning Book, Andriy Burkov, 2019

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Deep Learning

• Deep Learning, Ian Goodfellow et al., 2016
• Deep Learning with Python, François Chollet 2017
• Neural Networks and Deep learning, Micheal Nielsen, 2019
• Deep Learning and the Game of Go, Max Pumperla & Kevin Ferguson, 2019
• Dive into Deep Learning, Aston Zhang et al, 2020
• The fastai book, fast.ai, 2020

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Reinforcement Learning

• Reinforcement Learning: An Introduction - 2nd edition, Richard S. Sutton & Andrew G. Barto, 2018
• Algorithms for Reinforcement Learning, Csaba Szepesvári, 2019

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Meta-Learning

• Hands-On Meta Learning With Python, Sudharsan Ravichandiran, 2018

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Meta-Reasoning

• Metareasoning: Thinking about Thinking, Michael T. Cox et al., 2011

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Lifelong or Continual Learning

• Lifelong Machine Learning, Zhiyuan Chen & Bing Liu, 2nd edition, 2018

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Quantum Computing

• Quantum Computation and Quantum Information - 10th Anniversary Edition, Michael A. Nielsen & Isaac L. CHuang, 2010
• Dancing with Qubits: How quantum computing works and how it can change the world, Robert S. Sutor, 2019
• Quantum Computing: An Applied Approach, Jack D. Hilary, 2019

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Mathematics for Machine Learning

• Think Stats, Allen B. Downey, 2011
• Think Bayes: Bayesian Statistics in Python, Allen B. Downey, 2013
• Linear Algebra Done Right, 3rd edition, Sheldon Axler, 2015
• Practical Statistics for Data Scientists: 50 Essential Concepts, Peter Bruce & Andrew Bruce, 2017
• Math for Scientists Refreshing the Essentials, Maurits Natasha & Ćurčić-Blake Branislava, 2017
• Mathematics for Machine Learning, Marc Peter Deisenroth & A. Aldo Faisal & Cheng Soon Ong, 2019

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Causality

• Causal Inference in Statistics: A Primer, Judea Pearl & Madelyn Glymour & Nicholas P. Jewell, 2016
• The Book Of Why, Judea Pearl, 2018

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Computational Neuroscience

• Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems, Peter Dayan & Laurence F. Abbott, 2005
• Fundamentals of Computational Neuroscience, Thomas Trappenberg, 2009
• From Neuron to Brain, John G. Nicholls et al., 2011
• From Neuron to Cognition via Computational Neuroscience, Nicolas Brunel et al., 2016

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Data Analyse and Data Science

• Machine Learning for Hackers: Case Studies and Algorithms to Get You Started, Drew Conway & John Myles, 2012
• Python Data Science Handbook: Essential Tools for Working with Data, Jake VanderPlas, 2016
• Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, Wes McKinney, 2017
• Data Science from Scratch: First Principles with Python, 2nd edition, Joel Grus, 2019

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Additional Books

• Markov Decision Processes: Discrete Stochastic Dynamic Programming, Martin L. Puterman, 2005
• A Brief Introduction to Neural Networks, David Kriesel, 2007
• The Quest for Artificial Intelligence: A History of Ideas and Achievements, Nils J. Nilsson, 2009
• Dance of the Photons: From Einstein to Quantum Teleportation, Anton Zeilinger, 2010
• Quantum Mechanics: The Theoretical Minimum (Theoretical Minimum 2), Leonard Susskind & Art Friedman, 2014
• Dynamic Programming and Optimal Control Vol I-II, Dimitri P. Bertsekas, 2012-2017
• Ethical Artificial Intelligence, Bill Hibbard, 2015
• Approximate Dynamic Programming Solving the curses of dimensionality, Warren B. Powell, 2015
• Bayesian Reasoning and Machine Learning, David Barber, 2017
• DEEP LEARNING Practical introduction with Keras, Jordi Torres, 2018
• Machine Learning Yearning: Technical Strategy for AI Engineers, In the Era of Deep Learning, Andrew Ng, 2018
• Hands-On Neuroevolution with Python, Iaroslav Omelianenko, 2019
• PyTorch 1.x Reinforcement learning Cookbook, Yuxi (Hayden) Liu, 2019
• PyTorch Deep Learning Hands-on, Sherin Thomas & Sudhanshu Passi, 2019
• Deep Learning with Tensorflow 2 and Keras, 2nd edition, Antonio Gulli & Amita Kapoor & Sujit Pal, 2019
• Hands-on Neural Network with Tensorflow 2, Paolo Galeone, 2019
• Responsible Artificial Intelligence, Dignum, Virginia, 2019
• Interpretable Machine Learning, Christoph Molnar, 2020
• d2l, Aston Zhang et al, 2020

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Articles

Repositories

An important list of materials about Computer Vision

• Awesome computer vision, jbhuang0604

An important list of materials about Deep Learning in Computer Vision

• Awesome deep vision, kjw0612

An important list of materials about Recurrent Neural Network

• Awesome rnn, kjw0612

An important list of materials about Random Forest

• Awesome random forest, kjw0612

Terry Taewoong Um has a github page with the most important papers until 2017

• Deep Learning papers, Terry Taewoong Um

floodsung has created a papers reading roadmap for deep learning for newcomers (papers + books)

• Deep Learning Papers Reading Roadmap, floodsung

Open AI maintained a web page with the most import paper in RL

• Open AI spinningup

Denny Britz share papernotes on Deep Learning articles from 2011 to 2018-02

• Deep Learning Papernotes, Denny Britz

Long list of papers in ML, DL and NLP

• AI-NLP-Paper-Readings, zhongpeixiang

Sung Ju Hwang offers a big repository of artificial intelligence papers

• AdvancedML - reading list for artificial intelligence papers, Sung Ju Hwang

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Artificial Intelligence

• On Computable Numbers, with an Application to the Entscheidungsproblem, Alan M. Turing, 1936
• Intelligent Machinery, Alan M Turing, 1948
• Computing Machinery and Intelligence, Alan M. Turing, 1950
• A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, John NcCarthy at al, 1955
• Fuzzy sets, L.A.Zadeh, 1965
• Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability, Marcus Hutter, 2004
• Learning deep architectures for AI, Yoshua Bengio, 2009
• Deep Machine Learning—A New Frontier in Artificial Intelligence Research, Itamar Arel & Derek C. Rose & Thomas P. Karnowski, 2010
• On the Measure of intelligence, François Chollet, 2019
• The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence, Gary Marcus, 2020
• Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective, Luis Lamb et al. 2020
• AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence, Jeff Clune, 2020

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Machine Learning

• LIII. On lines and planes of closest fit to systems of points in space, Karl Pearson, 1901
• An Inductive Inference Machine, Ray Solomonoff, 1957
• Language identification in the limit, E. Mark Gold, 1967
• On the uniform convergence of relative frequencies of events to their probabilities, V. Vapnik & A. Chervonenkis, 1971
• Maximum likelihood from incomplete data via the EM algorithm, A P Dempster & N M Laird & D B Rubin, 1977
• A theory of the learnable, Leslie Valiant, 1984
• Learning representations by back-propagating errors, David E. Rumelhart & Geoffrey E. Hinton & Ronald J. Williams, 1986
• Induction of Decision Trees, J.R. Quinlan, 1986
• Learning Quickly When Irrelevant Attributes Abound: A New Linear-threshold Algorithm, Nick Littlestone, 1988
• Learning to predict by the method of Temporal difference, Richard S. Sutton, 1988
• The strength of weak learnability, Robert E. Schapire, 1990
• A training algorithm for optimum margin classifiers, Bernhard E. Boser & Isabelle M. Guyon & Vladimir N. Vapnik, 1992
• Multivariate Density Estimation, David W Scott, 1992
• Support-Vector Networks, Corinna Cortes & Vladimir Vapnik, 1995
• A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, Yoav Freund & Robert E Schapire, 1997
• Introduction to Gaussian Processes, David J. C. Macklay, 1998
• An Overview of Statistical Learning Theory, V Vapnik, 1999
• Improved Boosting Algorithms Using Confidence-rated Predictions, Robert E. Schapire & Yoram Singer, 1999
• Boosting Algorithms as Gradient Descent, Llew Mason et al, 2000
• Additive logistic regression: a statistical view of boosting, J. H. Friedman & T. Hastie & R. Tibshirani, 2000
• Random Forests, Leo Breiman , 2001
• Estimating the number of clusters in a data set via the ap statistic, Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001
• Learning with Kernels Support Vector Machines, Regularization, Optimization, and Beyond, Bernhard Schölkopf & Alexander J. Smola, 2002
• Latent Dirichlet Allocation, David M. Blei & Andrew Y. Ng & Michael I. Jordan, 2003
• An Introduction to Variable and Feature Selection, Isabelle Guyon & Andre Elisseeff, 2003
• A fast learning algorithm for deep belief nets, Geoffrey E. Hinton & Simon Osindero & Yee-Whye Teh, 2006
• Representation Learning: A Review and New Perspectives, Yoshua Bengio & Aaron Courville & Pascal Vincent, 2012
• A guide to convolution arithmetic for deep learning, Vincent Dumoulin & Francesco Visin, 2018
• Online Learning Rate Aadaptation With Hypergradient Descent, Atılım Gunes Baydin et al., 2018
• Tackling Climate Change with Machine Learning, David Rolnick et al., 2019
• AutoML-Zero: Evolving Machine Learning Algorithms From Scratch, Esteban Real et al., 2020

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Deep Learning

• A logical calculus of the ideas immanent in nervous activity, Warren S. McCulloch & Walter Pitts, 1943
• The perceptron: A probabilistic model for information storage and organization in the brain, F. Rosenblatt, 1958
• A Threshold Selection Method from Gray-Level Histograms, Nobuyuki Otsu, 1979
• A computational approach to edge detection, J Canny, 1986
• A tutorial on hidden Markov models and selected applications in speech recognition, R Rabiner, 1989
• Artificial neural networks, back propagation, and the Kelley-Bryson gradient procedure, Stuart E. Dreyfus, 1990
• A fast learning algorithm for deep belief nets, Geoffrey E. Hinton & Simon Osindero & Yee-Whye Teh, 1995
• Long Short-Term Memory, Sepp Hochreiter & Jürgen Schmidhuber, 1997
• Gradient-Based Learning Applied to Document Recognition, Yann LeCun, 1998
• A Neural Probabilistic Language Model, Yoshua Bengio et al., 2003
• Distinctive Image Features from Scale-Invariant Keypoints, David G. Lowe, 2004
• Framewise phoneme classification with bidirectional LSTM and other neural network architectures, Graves, Alex & Jürgen Schmidhuber, 2005
• Reducing the Dimensionality of Data with Neural Networks, G. E. Hinton and R. R. Salakhutdinov, 2006
• Self-taught Learning: Transfer Learning from Unlabeled Data, Rajat Raina et al., 2007
• A Novel Connectionist System for Unconstrained Handwriting Recognition Alex Grave et al., 2008
• Deep Boltzmann Machines, Ruslan Salakhutdinov & Geoffrey Hinton, 2009
• Measuring Invariances in Deep Networks Ian J. Goodfellow et al., 2009
• Deep, Big, Simple Neural Nets for Handwritten Digit Recognition, Dan Claudiu Cireşan et al., 2010
• Learning Convolutional Feature Hierarchies for Visual Recognition, Koray Kavukcuoglu et al., 2010
• A Committee of Neural Networks for Traffic Sign Classification, Dan Ciresan, 2011
• Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection, Richard Socher et al., 2011
• Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions, Richard Socher et al., 2011
• Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach, Xavier Glorot & Antoine Bordes & Yoshua Bengio, 2011
• Natural Language Processing (Almost) from Scratch, Ronan Collobert et al., 2011
• Building High-level Features Using Large Scale Unsupervised Learning, Quoc V. Le et al., 2012
• Large Scale Distributed Deep Networks, Jeffrey Dean et al., 2012
• Unsupervised and Transfer Learning Challenge: a Deep Learning Approach, Grégoire Mesnil et al.,2012
• Large-Scale Feature Learning With Spike-and-Slab Sparse Coding, Ian J. Goodfellow & Aaron Courville & Yoshua Bengio, 2012
• Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing, Antoine Bordes et al., 2012
• ImageNet Classification with Deep Convolutional Neural Networks, Alex Krizhevsky & Ilya Sutskever & Geoffrey E. Hinton, 2012
• Multi-column Deep Neural Networks for Image Classification, Dan Ciresan & Ueli Meier & Jurgen Schmidhuber, 2012
• Better Mixing via Deep Representations, Yoshua Bengio et al., 2012
• Distributed Representations of Words and Phrases and their Compositionality, Tomas Mikolov et al., 2013
• Learning Hierarchical Features for Scene Labeling, Clement Farabet et al., 2013
• Visualizing and Understanding Convolutional Networks, by Matt Zeiler & Rob Fergus, 2013
• Rich feature hierarchies for accurate object detection and semantic segmentation Tech report, Ross Girshick et al., 2013
• Speech Recognition With Deep Recurrent Neural Networks, Alex Graves & Abdel-rahman Mohamed & Geoffrey Hinton, 2013
• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation, Kyunghyun Cho et al., 2014
• Sequence to Sequence Learning with Neural Networks, Ilya Sutskever & Oriol Vinyals & Quoc V. Le, 2014
• Neural Machine Translation by Jointly Learning to Align and Translate, Dzmitry Bahdanau & Kyunghyun Cho & Yoshua Bengio, 2014
• Multimodal learning with deep Boltzmann machines, Nitish Srivastava & Ruslan Salakhutdinov, 2014
• Generative Adversarial Nets, Ian J. Goodfellow et al, 2014
• Dropout: A Simple Way to Prevent Neural Networks from Overfitting, Nitish Srivastava et al, 2014
• Very Deep Convolutional Networks For Large-Scale Image Recognition, Karen Simonyan & Andrew Zisserman, 2014
• Long-term recurrent convolutional networks for visual recognition and description, Jeff D. et al., 2014
• Deep Visual-Semantic Alignments for Generating Image Descriptions, Andrej Karpathy & Li Fei-Fei, 2014
• Spatial Transformer Networks, Max Jaderberg et al., 2015
• Fast R-CNN, Ross Girshick, 2015
• The Netflix Recommender System: Algorithms, Business Value, and Innovation, CARLOS A. GOMEZ-URIBE and NEIL HUNT, 2015
• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, Shaoqing Ren et al., 2015
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, Sergey Ioffe & Christian Szegedy, 2015
• Character-level convolutional networks for text classification, Xiang Z. & Junbo Jake Z. & Yann L. 2015
• U-Net: Convolutional Networks for Biomedical Image Segmentation, Olaf R. & Philipp F. & Thomas B.,2015
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, Alec R. & Luke M. & Soumith C., 2015
• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, Shaoqing R. et al., 2015
• Deep Learning, Yann LeCun & Yoshua Bengio & Geoffrey Hinton, 2015
• Going Deeper with Convolutions, Christian Szegedy et al., 2015
• Deep Residual Learning for Image Recognition, Kaiming He et al., 2015
• Distilling the Knowledge in a Neural Network, Geoffrey Hinton & Oriol Vinyals & Jeff Dean, 2015
• Deep Learning in Neural Networks: An Overview, Juergen Schmidhuber, 2015
• Deep Residual Learning for Image Recognition, Kaiming He et al, 2016
• TensorFlow: a system for large-scale machine learning, Martín A. el al, 2016
• Attention is all you need, Ashish Vaswani et al., 2017
• Dynamic Routing Between Capsules, Sara Sabour & Nicholas Frosst & Geoffrey E Hinton, 2017
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, Jacob Devlin et al., 2018
• Neural Ordinary Differential Equations, Ricky T. Q. Chen et al., 2018
• XLNet: Generalized Autoregressive Pretraining for Language Understanding, Zhilin Yang et al, 2019
• RILOD: Near Real-Time Incremental Learning for Object Detection at the Edge, Dawei Li et al., 2019
• PyTorch: An Imperative Style, High-Performance Deep Learning Library, Adam Paszke et al, 2019
• Mesh R-CNN, Georgia Gkioxari & Jitendra Malik & Justin Johnson, 2019
• Taxonomy of Real Faults in Deep Learning Systems, Nargiz Humbatova et al., 2019
• Modeling yield response to crop management using convolutional neural networks, Alexandre Barbosa et al., 2020
• Explainable Deep Learning: A Field Guide for the Uninitiated, Ning Xie et al., 2020

An exhaustive list can be found in the Deep Learning, Goodfellow I. et al, 2016:

• Deep Learning, Goodfellow I. et al, 2016 - bibliography

An exhaustive list about neural network search can be available here:

• Literature on Neural Architecture Search, Marius Lindauer, currently maintained

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Knowledge Graphs and Knowledge Graph Nets

• RESCAL: A Three-Way Model for Collective Learning on Multi-Relational Data, Maximilian Nickel and Volker Tresp and Hans-Peter Kriegel, 2011
• Representation Learning: A Review and New Perspectives, Yoshua Bengio and Aaron Courville and Pascal Vincent, 2012
• NTN: Reasoning With Neural Tensor Networks for Knowledge Base Completion, Richard Socher el al., 2013
• TransH: Knowledge Graph Embedding by Translating on Hyperplanes, Zhen Wang et al., 2014
• TransR & CTransR: Learning Entity and Relation Embeddings for Knowledge Graph Completion, Yankai Lin et al., 2015
• A Review of Relational Machine Learning for Knowledge Graphs, Maximilian Nickel et al., 2015
• HolE: Holographic Embeddings of Knowledge Graphs, Maximilian Nickel and Lorenzo Rosasco and Tomaso A. Poggio, 2016
• TranSparse: Knowledge Graph Completion with Adaptive Sparse Transfer Matrix, Guoliang Ji et al., 2016
• GAKE: Graph Aware Knowledge Embedding, Jun Feng et al., 2016
• ProPPR: Learning First-Order Logic Embeddings via Matrix Factorization, William Yang Wang and William W. Cohen, 2016
• Knowledge Graph Embedding: A Survey of Approaches and Applications, Quan Wang et al., 2017
• SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptions, Han Xiao et al., 2017
• ProjE: Embedding Projection for Knowledge Graph Completion, Baoxu Shi and Tim Weninger, 2017
• ITransF: An Interpretable Knowledge Transfer Model for Knowledge Base Completion, Qizhe Xie et al., 2017
• Convolutional 2D Knowledge Graph Embeddings, Tim Dettmers et al., 2018
• Accurate Text-Enhanced Knowledge Graph Representation Learning, Bo An et al., 2018
• ConvKB: A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network, Dai Quoc Nguyen et al., 2018
• Modeling Relational Data with Graph Convolutional Networks, Michael Schlichtkrull et al., 2018
• Canonical Tensor Decomposition for Knowledge Base Completion, Timothée Lacroix and Nicolas Usunier and Guillaume Obozinski, 2018
• Improving Knowledge Graph Embedding Using Simple Constraints, Boyang Ding et al., 2018
• Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs, Deepak Nathani et al., 2019
• RSN: Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs, Lingbing Guo and Zequn Sun and Wei Hu, 2019
• CapsE:A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization, Dai Quoc Nguyen et al., 2019
• CaRe: Open Knowledge Graph Embeddings, Swapnil Gupta and Sreyash Kenkre and Partha Talukdar, 2019
• Knowledge Graphs, Aidan Hogan et al., 2020
• Benchmarking Graph Neural Networks, Vijay Prakash Dwivedi et al., 2020

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Reinforcement Learning

A short list

• Reinforcement Learning, an introduction, R Sutton & A Barto, 1998
• Playing Atari with Deep Reinforcement Learning, Mnih et al, 2013
• Recurrent Models of Visual Attention, Volodymyr Mnih et al., 2014
• Deep Recurrent Q-Learning for Partially Observable MDPs, Hausknecht and Stone, 2015
• Deep Reinforcement Learning with Double Q-learning, Hasselt et al 2015
• Prioritized Experience Replay, Schaul et al, 2015
• Dueling Network Architectures for Deep Reinforcement Learning, Wang et al, 2015
• Human-level control through deep reinforcement learning, Volodymyr M. et al 2015
• Asynchronous methods for deep reinforcement learning, Volodymyr M., et al., 2016
• Neural Architecture Search with Reinforcement Learning, Barret Zoph & Quoc V. Le, 2016
• Rainbow: Combining Improvements in Deep Reinforcement Learning, Hessel et al, 2017
• Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm, David Silver et al., 2017
• Mastering the game of Go without human knowledge, David Silver et al., 2017
• Grandmaster level in StarCraft II using multi-agent reinforcement learning, Oriol Vinyals et al, 2019
• Emergent Tool Use From Multi-Agent Autocurricula, Bowen Baker et al., 2019
• SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards, Siddharth Reddy and Anca D. Dragan and Sergey Levine, 2019
• Reinforcement Learning for Combinatorial Optimization: A Survey, Nina Mazyavkina et al., 2020
• Chip Placement with Deep Reinforcement Learning, Azalia Mirhoseini et al., 2020

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Meta-Learning

• Neural Turing Machines, Alex Graves & Greg Wayne & Ivo Danihelka, 2014
• Siamese Neural Networks for One-shot Image Recognition, Gregory Koch & Richard Zemel & Ruslan Salakhutdinov, 2015
• Meta-Learning with Memory-Augmented Neural Networks, Adam Santoro et al., 2016
• Matching Networks for One Shot Learning, Oriol Vinyals et al., 2016
• Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks, Chelsea Finn & Pieter Abbeel & Sergey Levine, 2017
• Meta Networks, Tsendsuren Munkhdalai & Hong Yu, 2017
• Learning to Reinforcement Learn, Jane X Wang et al., 2017
• Prototypical Networks for Few-shot Learning, Jake Snell & Kevin Swersky & Richard Zemel, 2017
• Optimization As A Model For Few-Shot Learning, Sachin Ravi & Hugo Larochelle, 2017
• Learning to Compare: Relation Network for Few-Shot Learning, Flood Sung et al., 2018
• On First-Order Meta-Learning Algorithms, Alex Nichol & Joshua Achiam & John Schulman, 2018
• Meta-Learning: A Survey¸, Joaquin Vanschoren, 2018
• Learning to Continually Learn, Shawn Beaulieu, 2020

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Meta-reasoning

• On Optimal Game-Tree Search using Rational Meta-Reasoning, Stuart Russell & Eric Wefald, 1989
• Metareasoning, Stuart J. Russell, 1997
• Definition and Complexity of Some Basic Metareasoning Problems, Vincent Conitzer & Tuomas Sandholm, 2003
• Visualization of Meta-Reasoning in Multi-Agent Systems, D. Řehoř & J. Tožička & P. Slavík, 2005
• Meta-reasoning: What can we learn from meta-memory?, Rakefet Ackerman & Valerie A. Thompson, 2015
• Meta-Reasoning: Monitoring and Control of Thinking and Reasoning, Rakefet Ackerman & Valerie A. Thompson, 2017
• Meta-reasoning: Shedding metacognitive light on reasoning research, Rakefet Ackerman & Valerie A. Thompson, 2018
• Rational metareasoning and the plasticity of cognitive control, Falk Lieder et al., 2018
• Meta Reasoning over Knowledge Graphs, Hong Wang et al., 2019
• Doing more with less: meta-reasoning and meta-learning in humans and machines, Thomas L Griffiths et al., 2019

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Consciousness

• The Importance of Quantum Decoherence in Brain Processes, Max Tegmark, 1999
• Non-Computability of Consciousness, Daegene Song, 2007
• Mathematical Foundations of Consciousness, Willard L. Miranker, Gregg J. Zuckerman, 2008
• Do Artificial Reinforcement-Learning Agents Matter Morally? Brian Tomasik, 2014
• Is Intelligence Artificial? Kieran Greer, 2015
• Ascribing Consciousness to Artificial Intelligence, Murray Shanahan, 2015
• The method of artificial systems, Christopher A. Tucker, 2015
• Non-Evolutionary Superintelligences Do Nothing, Eventually, Telmo Menezes, 2016
• An affective computational model for machine consciousness, Rohitash Chandra, 2017
• Living Together: Mind and Machine Intelligence, Neil D. Lawrence, 2017
• The Limits to Machine Consciousness, Subhash Kak, 2017
• The Morphospace of Consciousness, Xerxes D. Arsiwalla, 2018
• Conscious Enactive Computation, Daniel Estrada, 2018
• The Consciousness Prior, Yoshua Bengio, 2019
• A theory of consciousness: computation, algorithm, and neurobiological realization, J. H. van Hateren, 2019
• A Mathematical Framework for Superintelligent Machines, Daniel J. Buehrer, 2019
• The meta-problem and the transfer of knowledge between theories of consciousness: a software engineer’s take, Marcel Kvassay, 2019
• The Mode of Computing, Luis A. Pineda, 2019
• Neural Consciousness Flow, Xiaoran Xu et al., 2019
• Secrets of the Brain: An Introduction to the Brain Anatomical Structure and Biological Function, Jiawei Zhang, 2019
• Consciousness and Automated Reasoning, Ulrike Barthelme and Ulrich Furbach and Claudia Schon, 2020
• A Machine Consciousness architecture based on Deep Learning and Gaussian Processes, Eduardo C. Garrido Merchan and Martin Molina, 2020
• The Mathematical Structure of Integrated Information Theory, Johanes Kleiner and Sean Tull, 2020
• ConsciousControlFlow(CCF): A Demonstration for conscious Artificial Intelligence Hongzhi Wang et al., 2020
• Will we ever have Conscious Machines? Patrick Krauss and Andreas Maier, 2020

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Federated Learning

• Federated Learning: Strategies For Improving Communication Efficiency, Jakub Konecny et al., 2016
• Federated Optimization: Distributed Machine Learning for On-Device Intelligence, Jakub Konecny et al., 2016
• Communication-Efficient Learning of Deep Networks from Decentralized Data, H. Brendan McMahan et al., 2017
• Federated Multi-Task Learning, Virginia Smith et al., 2017
• Agnostic Federated Learning, Mehryar Mohri and Gary Sivek and Ananda Theertha Suresh, 2019
• Bayesian Nonparametric Federated Learning of Neural Networks, Mikhail Yurochkin et al., 2019
• Towards Federated Learning At Scale: System Design, Keith Bonawitz et al., 2019
• Federated Optimisation In Heterogeneous Networks, Tian Li et al., 2020
• Federated Learning with Matched Averaging, Hongyi Wang et al., 2020
• On the Convergence of FedAvg on Non-IID Data, Xiang Li et al., 2020

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Lifelong or Continual Learning

• A Lifelong Learning Perspective for Mobile Robot Navigation, Sebastian Thrun, 1995
• CHILD: A First Step Towards Continual Learning, Mark B. Ring, 1997
• An Approach to Lifelong Reinforcement Learning through Multiple Environments, Fumihide Tanaka & Masayuki Yamamura, 1997
• Multi-Task Reinforcement Learning: A Hierarchical Bayesian Approach, Aaron Wilson et al., 2007
• Horde: a scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction, Richard S. Sutton et al., 2011
• ELLA: An Efficient Lifelong Learning Algorithm, Paul Ruvolo & Eric Eaton, 2013
• Active Task Selection for Lifelong Machine Learning, Paul Ruvolo & Eric Eaton, 2013
• Lifelong Machine Learning Systems: Beyond Learning Algorithms, David L. Silver & Qiang Yang & Lianghao Li, 2013
• A PAC-Bayesian bound for Lifelong Learning, Anastasia Pentina & Christoph H. Lampert, 2013
• Multi-timescale Nexting in a Reinforcement Learning Robot, Josepth Modayil & Adam White & Richard S. Sutton, 2014
• Online Multi-Task Learning for Policy Gradient Methods, Haitham Bou Ammar et al., 2014
• Safe policy search for lifelong reinforcement learning with sublinear regret, Haitham Bou Ammar & Rasul Tutunov & Eric Eaton, 2015
• Lifelong Learning with Non-i.i.d. Tasks, Anastasia Pentina & Christoph H. Lampert, 2015
• Lifelong Learning for Sentiment Classification, Zhiyuan Chen & Nianzu Ma & Bing Liu, 2015
• Autonomous cross-domain knowledge transfer in lifelong policy gradient reinforcement learning, Haitham Bou Ammar et al., 2015
• Lifelong Learning with Weighted Majority Votes, Anastasia Pentina & Ruth Urner, 2016
• Lifelong Learning for Disturbance Rejection on Mobile Robots, David Isele et al., 2016
• Generalized Dictionary for Multitask Learning with Boosting, Boyu Wang & Joelle Pineau, 2016
• Learning Cumulatively to Become More Knowledgeable, Geli Fei & Shuai Wang & Bing Liu, 2016
• Using task features for zero-shot knowledge transfer in lifelong learning, David Isele & Mohammad Rostami & Eric Eaton, 2016
• A Deep Hierarchical Approach to Lifelong Learning in Minecraft, Chen Tessler et al. 2016
• Progressive Neural Networks, Andrei A. Rusu et al., 2016
• Overcoming catastrophic forgetting in neural networks, James Kirkpatrick et al., 2016
• Continual Lifelong Learning with Neural Networks: A Review, German I. Paris et al., 2018

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Explainability, interpretability

• The Mythos of Model Interpretability, Zachary C. Lipton, 2016
• “Why Should I Trust You?” Explaining the Predictions of Any Classifier, Marco Tulio Ribeiro and Sameer Singh and Carlos Guestrin, 2016
• RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism, Edward Choi et al., 2016
• Towards A Rigorous Science of Interpretable Machine Learning, Finale Doshi-Velez and Been Kim, 2017
• What do we need to build explainable AI systems for the medical domain?, Andreas Holzinger et al., 2017
• Attentive Explanations: Justifying Decisions and Pointing to the Evidence, Dong Huk Park et al., 2017
• Transparency: Motivations and Challenges, Adrian Weller, 2017
• Explaining Explanations in AI, Brent Mittelstadt and Chris Russell and Sandra Wachter, 2018
• AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias, Rachel K. E. Bellamy et al., 2018
• Techniques for Interpretable Machine Learning, Mengnan Du and Ninghao Liu and Xia Hu, 2018
• Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead, Cynthia Rudin, 2018
• On the Art and Science of Explainable Machine Learning: Techniques, Recommendations, and Responsibilities, Patrick Hall, 2018
• A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti et al., 2018
• A comparative study of fairness-enhancing interventions in machine learning, Sorelle A. Friedler et al., 2018
• 50 Years of Test (Un)fairness: Lessons for Machine Learning Ben Hutchinson and Margaret Mitchell, 2018
• Explaining Explanations: An Overview of Interpretability of Machine Learning, Leilani H. Gilpin et al., 2019
• A Marauder’s Map of Security and Privacy in Machine Learning: An overview of current and future research directions for making machine learning secure and private, Nicolas Papernot, 2018
• Interpretable machine learning: definitions, methods, and applications, W. James Murdoch et al., 2019
• Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI Shane T. Mueller et al., 2019

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Quantum Computing

Quantum Computing

A very good repository: Awesome Quantum Computing is offer by Desiree Vogt-Lee and a lot of contributors - Awesome Quantum Computing, Desiree Vogt-Lee et al.

• Optimization by simulated annealing, S Kirkpatrick & C D Gelatt & M P Vecchi., 1983
• Quantum mechanical computers, Richard P. Feynman, 1986
• The Solovay-Kitaev algorithm, 1997
• Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer, Peter W. Shor, 1995
• Good quantum error-correcting codes exist, A. R. Calderbank & Peter W. Shor, 1996
• Fault-tolerant quantum computation by anyons, A. Yu. Kitaev, 1997
• Quantum Computation by Adiabatic Evolution, Edward Farhi, 2000

Quantum Approximate Optimization Algorithm

• A Quantum Approximate Optimization Algorithm, Edward Farhi & Jeffrey Goldstone, 2014
• Improving Variational Quantum Optimization using CVaR, Panagiotis Kl. Barkoutsos et al, 2019
• Evaluating Quantum Approximate Optimization Algorithm: A Case Study, Ruslan Shaydulin & Yuri Alexeev, 2019
• Analysis of Quantum Approximate Optimization Algorithm under Realistic Noise in Superconducting Qubits, Mahabubul Alam & Abdullah Ash-Saki & Swaroop Ghosh, 2019

Quantum Machine Learning

• Quantum Deep Learning, Nathan Wiebe & Ashish Kapoor & Krysta M. Svore, 2014
• Quantum Inspired Computational Intelligence, Siddhartha Bhattacharyya & Ujjwal Maulik & Paramartha Dutta, 2017
• Quantum Kitchen Sinks: An algorithm for machine learning on near-term quantum computers, C. M. Wilson et al, 2018
• Hierarchical quantum classifiers, Edward Grant et al, 2018
• Quantum Graph Neural Networks, Guillaume Verdon et al, 2019
• Quantum circuit structure learning, Mateusz Ostaszewski & Edward Grant & Marcello Benedett, 2019
• TensorFlow Quantum: A Software Framework for Quantum Machine Learning, Michael Broughton et al., 2020

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Causality

• Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Judea Pearl, 1988

In progress...

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Computational Neuroscience

• A Framework for Intelligence and Cortical Function Based on Grid Cells in the Neocortex, Jeff Hawkins, 2019 or this link

In progress

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Others

• A Mathematical Theory of Communication, C. E. Shannon, 1949
• Introduction to Algorithms, T H Cormen et al 1990
• Computational Complexity: A Modern Approach, Sanjeev Arora & Boaz Barak, 2007
• Open-endedness: The last grand challenge you’ve never heard of, Kenneth Stanley et al., 2017

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Posts and thesis

Blog posts

• Markov Chain Monte Carlo Without all the Bullshit, Jeremy Kun, 2015
• Learning Policies For Learning Policies — Meta Reinforcement Learning (RL²) in Tensorflow, Arthur Juliani, 2017
• Learning to learn, Berkeley Artificial Intelligence Research, 2017
• Reinforcement Learning, Lilian Weng, 2018
• Collection of materials for Newbie in Deep Learning and Machine Learning and Data Science, Oleg Gorodnitchi, 2018
• Meta-Learning: Learning to Learn Fast, Lilian Weng, 2018
• Understanding Neural Networks: What, How and Why? Unraveling the black box, Euge Inzaugarat, 2018
• Backpropagation 101, thinc.ai
• Meta-Reinforcement Learning, Michaël Trazzi, 2019
• Meta-Reinforcement Learning, Lilian Weng, 2019
• Meta-Learning: from Few-Shot Learning to Rapid Reinforcement Learning ICML 2019 Tutorial, Chelsea Finn & Sergey Levine, 2019
• A Deep Dive into Reinforcement Learning, Adam Stelmaszczyk
• Brief History of Neural Networks
• Federated Learning
• Meta-Reasoning, Georgia Tech OMSCS
• The Matrix Calculus You Need For Deep Learning, Terence Parr & Jeremy Howard
• Visualizing machine learning one concept at a time, Jay Alammar
• The Illustrated Transformer, Jay Alammar
• The Transformer Family, Lilian Weng, 2020
• Attention? Attention!, Lilian Weng, 2018
• The mathematics of optimization for deep learning, Tivadar Danka, 2020

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Thesis

• Evolutionary principles in self-referential learning, or on learning how to learn, J. Schmidhuber, 1987
• Continual learning in Reinforcement Learning environments, Mark Bishop Ring, 1994
• Explanation-Based Neural Network Learning: A Lifelong Learning Approach Sebastian Thrun, 1996
• Statistical Language Models based on Neural Networks, Mikolov Tomáš, 2012
• Training Recurrent Neural Networks, Ilya Sutskever, 2012
• Statistical Language Models based on Neural Networks, Mikolov Tomáš, 2012
• Rational Approaches to Learning and Development, Celeste Kidd, 2013
• Metareasoning and Mental Simulation, Jessica B. Hamrick, 2017
• Neural Transfer Learning for Natural Language Processing, Sebastian Ruder, 2019

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Scientific Research

Research guide

The two references inside the article are very interesting

• An Opinionated Guide to ML Research, John Schulman
• How to Be a Researcher - 2nd Edition, Jonathan St B. T. Evans, 2015
• How to write and publish your research
• How to write a research paper, Science Direct
• Google's Hybrid Approach to Research, Alfred Spector, Peter Norvig, Slav Petrov, 2013
• Google's Brain Team Approach
• How to read a scientific paper, Christophe Pere, 2019
• How to write a technical paper, Michael Ernst, 2019
• Getting started with Git and GitHub
• Slow-Science manifesto, take time to do good science
• Build stuff!, 2020
• How to summarize a research article, University of Washington

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Research Papers & Academic Resources

• Computer Science, ArXiv
• Arxiv Sanity Preserver
• Paper With Codes
• Google.ai
• Facebook
• DeepAI Research
• Journal of Artificial Intelligence
• Google Scholar
• MIT - Machine Learning
• Science Direct
• Nature - machine learning
• Academia.edu - machine learning
• University of Oxford - AI & ML
• Caltech Institute of Technology (CIT)
• Berkeley - Medium
• The Very Large Data Base Journal (VLDB Journal)

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NLP

• NLP-progress, Sebastian Ruder

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List of most cited researchers

• Machine Learning
• Deep Learning
• Reinforcement Learning
• Artificial Intelligence

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People to follow

| Researchers | Arxiv | Google Scholar | GitHub | | :---:| :---: | :---: | :---: | | Aaron Courville | link | link | | | Adam Coates | - | link | | | Andrej Karpathy | - | link | | | Andrew Barto | - | link | | | Andrew Ng | link | link | | | Christopher D. Manning | link | link | | | Daphne Koller | - | link | | | David Silver | -| link | | | Doina Precup | link | link | | | François Chollet | link | link | | | Gary Marcus |- | -| | | Geoffrey Hinton | link | link | | | Hugo Larochelle | link | link | | | Ian Googfellow | link | link | | | Judea Pearl | | link | | | Jürgen Schmidhuber | -| link | | | Justin Johnson | | link | link| | Li Fei-Fei | | link| | | Michael I. Jordan | - | link | | | Nando de Freitas |- | link | | | Oriol Vinyals | link | link | | | Peter Norvig | - | link | | | Richard S. Sutton | link | link | | | Sebastian Ruder | link | link | | | Stuart Russell | - | link | | | Terrence Sejnowski |- | link | | | Yann LeCun | link | link | | | Yoshua Bengio | link | link | |

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AI communities

• AI Dreams
• Forum for Artificial Intelligence
• Quora
• Reddit Groups: r/MachineLearning, r/DeepLearning, r/DataScience, r/learnmachinelearning r/artificial/
• Stack Overflow
• Kaggle
• Jupyter Community
• DEV
• ods.ai, Open Data Science
• fastai

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Resources

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Librairies

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PyTorch

Orinigal tutorials on pyTorch

• PyTorch
• Introduction to Pytorch Code Examples, Stanford

Here a list a lot of github resources on PyTorch, each repo contains an impressive collection of tutorials, books, papers etc.

• Ritchie Ng
• bharathgs
• gwding
• jekbradbury
• NurmaU
• Deep Learning With PyTorch ##### Notebooks
• PyTorch Primer

Tensorflow

Original tensorflow documentation and turorial

• Tensorflow 2.x

jtoy propose a github page with lot of resources, models, tutorials, books etc. on tensorflow

• jtoy

Ritchie Ng has began a github for TensorFlow 2

• Ritchie Ng
• TensorFlow Forum Discussion

Notebooks

• TensorFlow 2.0 + Keras Crash Course, François CHollet, 2019
• tf.keras for Researchers: Crash Course, François Chollet
• TensorFlow 2.0, Zaid Alyafeai

Packages

• Catalyst, Kolesnikov, Sergey, 2018
• trfl, Deepmind, 2019
• graph_nets, Graph Representation Learning, Data Science Group, IIT Roorkee
• transformers, hugging face
• reformers, google/trax
• The Abstraction and Reasoning Corpus (ARC), François Chollet, 2019
• Google AI Research
• Stanza: A Python NLP Library for Many Human Languages, Stanford NLP Group's, 2020
• DoWhy, Microsoft, 2019
• TCDF: Temporal Causal Discovery Framework, M-Nauta
• A gentle introduction to neurolib, Caglar Cakan
• pycaret
• RLlib: Scalable Reinforcement Learning

Notebooks

• Reformers, Trax Quick Intro
• PracticalAI, madewithml

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Quantum Computing

• Micheal Nielsen
• Pennylane, Xanadu
• Qiskit, IBM Resaerchsource code
• D-Wave Tutorials on Quantum Annealing
• TensorFlow Quantum, 2020

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Blogs

• Medium
• Lil'Log
• A free introduction to quantum computing and quantum mechanics, Andy Matuschak & Micheal Nielsen
• Musty Thoughts, Michał Stęchły
• Yoshua Bengio Blog
• Christopher Colah
• Sebastian Ruder Blog
• Distill - ML explanations
• DeepAI
• Open AI
• Microsoft Research
• Microsoft
• Facebook
• DeepMind
• Fast.ai
• Google AI blog
• Apple
• Amazon
• Baidu Research
• Intel AI
• Allen AI
• Element AI
• Cambridge Maths Blog
• Andrej Karpathy
• Nando de Freitas
• Daphne Koller
• Adam Coates
• Jürgen Schmidhuber
• Terrence Sejnowski
• Michael I. Jordan
• The Data Frog, Colin Bernet
• Daniel Lemire' Blog
• Berkeley Artificial Intelligence Research or this link
• Neptune.ai
• Machine Learning Blog
• Science Daily
• Hacker noon
• Machine Learning Medium feed
• Springboard Blog
• KDnuggets
• Analytics Vidhya
• O'Reilly
• Google News
• Deep Learning AI
• Sicara blog
• Frontiers in Computational Neuroscience

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Datasets

• Dataset Search, Google
• Google Research Datasets
• Stanford Datasets
• AWS Open Datasets
• Lionbridge Social Datasets
• Awesome Datasets, Xiaming
• Abstraction and Reasoning Corpus, François Chollet, 2019
• Pathmind Open datasets
• Kaggle Datasets
• NLP datasets, niderhoff

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Best-practices

• Rules of ML, Google
• Responsible AI Practices, Google AI
• Responsible AI, Microsoft
• Responsbile use AI, Canada Gouvernment
• Natural Language Processing best Practices

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Explainability, interpretability

• H2O.ai Interpretability
• Interpretability in machine learning, jphall11663

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Courses

MOOCs

• Quantum Machine Learning, edx
• Machine Learning, Coursera, Andrew Ng
• Reinforcement Learning Specialization, Coursera
• Deep Learning Specialization, Coursera
• Deep Reinforcement Learning, Berkeley
• Rinforcement Learning, UCL
• MIT OpenCourseWare
• edx
• Harvard Online
• Stanford Online
• Reinforcement Learning, Denny Britz

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Youtube

• Linear Algebra, MIT edu
• Machine Learning
• Machine Learning, Stanford, Andrew Ng
• Stanford CS231n, 2016
• Deep Learning, Stanford, 2018
• CS224N: Natural Language Processing with Deep Learning - Winter 2019
• Reinforcement Learning, Stanford, 2019
• Introduction to Reinforcement Learning, DeepMind
• Advanced Deep Learing and Reinforcement learning, DeepMind
• Sentdex
• Machine Learning, Josh Gordon
• Stanford’s Free Artificial Intelligence course now available online, Arun C, 2020
• CS 287: Advanced Robotics, Fall 2019, Berkeley
• Stanford CS330: Deep Multi-Task and Meta Learning
• Programming Methodology, Stanford
• Introduction to Computer Science I, Harvard
• MIT Deep Learning, Lex Fridman
• MIT 6.S191 - Introduction to Deep Learning, Alexander Amini & Ava Soleimany, 2020
• Neural networks class, Hugo Larochelle
• Deep Unsupervised Learning, Pieter Abbeel et al., 2020

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Support courses

• A Beginner's Guide to the Mathematics of Neural Networks, A.C.C. Coolen, 1998 or this link
• Introduction to Applied Linear Algebra, Stephen Boyd & Lieven Vandenberghe, 2018
• the-math-behind-an-artificial-neural-network, hugolgst
• Machine Learning From Scratch, Erik Linder-Norén, 2018
• Lot of material resources to learn Maths, Reddit r/learnmmath
• Linear Algebra, Reddit r/bibliographies

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Sites

• Practical Deep Learning for Coders, v3
• Machine Learning Mastery, Jason Brownlee
• Towards Data Science, Medium
• A.I. Wiki, Pathmind
• Computer Vision, Microsoft, 2020
• Deep-Learning drizzle, kmario23
• Meta-Reasoning
• Reinforcement Learning Tips and Tricks
• The Neural Network Zoo, Fjodor Van Veen, 2016
• Spacy Course, Ines Montani
• The perceptron neuron model, Roberto Lopez, 2020

Notebooks

• CS231n Python Tutorial With Google Colab, Justin Johnson

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General and technical additional books

General

AI

• The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World, Pedro Domingos, 2015
• Superintelligence: Paths, Dangers, Strategies, Nick Bostrom, 2016
• Architects of Intelligence: The truth about AI from the people building it, Martin Ford, 2018
• Life 3.0: Being Human in the Age of Artificial Intelligence, Max Tegmark, 2018
• There is no such thing as Artificial Intelligence, Luc julia, 2020

Other

• Thinking, Fast and Slow, Daniel Kahneman, 2013
• Homo Deus, Yuval Noah Harari, 2018
• How We Learn: Why Brains Learn Better Than Any Machine . . . for Now, Stanislas Daheane, 2020
• This will make you Smarter, edited by John Brockman, 2012

Technical

• The art of Computer Programming, Vol 1-2-3-4a, Donald E. Knuth
• Advanced Deep Learning with Python, Ivan Vasilev, 2019

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Contribution

Your contributions are always welcome!

If you want to contribute to this list (please do), send me a pull request or contact me @chris or chris

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Licence

To the extent possible under law, Christophe PERE has waived all copyright and related or neighboring rights to Computer Science References for researchers. This work is published from: Canada.