- https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/
- https://swcarpentry.github.io/python-novice-inflammation/
- https://runestone.academy/runestone/books/published/thinkcspy/index.html
- https://seeing-theory.brown.edu/
- https://www.khanacademy.org/
- https://codecademy.com/
- https://www.codewars.com/
- https://www.udacity.com/
- https://cs50.harvard.edu/
- https://leetcode.com/
- https://github.com/juanklopper/Statistics-Notes
- https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/
- https://www-bcf.usc.edu/~gareth/ISL/ISLR%20Seventh%20Printing.pdf
- https://github.com/jakevdp/WhirlwindTourOfPython
- https://github.com/rouseguy/intro2stats
- https://automatetheboringstuff.com/
- https://github.com/davecom/ClassicComputerScienceProblemsInPython
- https://github.com/wesm/pydata-book
- https://github.com/jakevdp/PythonDataScienceHandbook
- https://github.com/jdwittenauer/ipython-notebooks
- https://github.com/WillKoehrsen/Hands-On-Machine-Learning/tree/master/handson-ml-master
- https://github.com/ageron/handson-ml2
- https://explained.ai/matrix-calculus/
- https://github.com/fchollet/deep-learning-with-python-notebooks
- https://github.com/brinkar/real-world-machine-learning
- https://www.coursera.org/courses?query=machine+learning+andrew+ng
- http://web.stanford.edu/class/cs109/
- http://cs229.stanford.edu/
- https://cs231n.github.io/
- http://web.stanford.edu/class/cs20si/
- http://cs224d.stanford.edu/syllabus.html
- http://people.duke.edu/~ccc14/sta-663-2016/index.html
- http://info.usherbrooke.ca/hlarochelle/neural_networks/content.html
- http://www.fast.ai/
- https://github.com/the-deep-learners/deep-learning-illustrated
- https://github.com/DataScienceUB/DeepLearningMaster20192020
- https://github.com/ComputoCienciasUniandes/HerramientasComputacionales
- https://github.com/glouppe/info8010-deep-learning
- https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
- https://www.deeplearningbook.org/
- https://web.stanford.edu/~hastie/Papers/ESLII.pdf
- https://web.stanford.edu/~jurafsky/slp3/ed3book.pdf
- https://github.com/jack2684/cs76-ai/blob/master/Artificial Intelligence A Modern Approach 3rd Edition.pdf
- https://www.inference.org.uk/itprnn/book.pdf
- http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/090310.pdf
- https://mml-book.github.io/
- https://cbmm.mit.edu/lh-9-520/syllabus
- http://rail.eecs.berkeley.edu/deeprlcourse/
- https://github.com/PacktPublishing/Deep-Reinforcement-Learning-Hands-On
- http://web.stanford.edu/class/cs234/index.html
- https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf
- http://videolectures.net/DLRLsummerschool2018_toronto/
- https://github.com/dennybritz/reinforcement-learning
- https://spinningup.openai.com/en/latest/
- https://www.davidsilver.uk/teaching/
- https://github.com/kmario23/deep-learning-drizzle
- https://www.freecodecamp.org/news/ivy-league-free-online-courses-a0d7ae675869/
- https://paperswithcode.com/
- https://sgfin.github.io/learning-resources
- https://github.com/tmheo/deep_learning_study
- https://github.com/josephmisiti/awesome-machine-learning/blob/master/books.md
- https://montrealartificialintelligence.com/academy/
- https://metacademy.org/graphs/concepts/deep_belief_networks#focus=random_variables&mode=explore
- https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks
- https://github.com/ChristosChristofidis/awesome-deep-learning
- https://sgfin.github.io/learning-resources/
- https://github.com/firmai/financial-machine-learning
- https://github.com/brylevkirill/notes
- https://github.com/rsvp/fecon235
- https://python.quantecon.org/index_toc.html
- https://github.com/jlcatonjr/Learn-Python-for-Stats-and-Econ/blob/master/Learn%20Python%20for%20Economic%20Computation%20-%20A%20Crash%20Course.pdf
- https://github.com/jmbejara/comp-econ-sp18
- https://github.com/rasbt/python-machine-learning-book/tree/master/code
- https://github.com/grananqvist/Awesome-Quant-Machine-Learning-Trading
- https://github.com/jgerardsimcock/trading
- https://github.com/stefan-jansen/machine-learning-for-trading
- https://github.com/jstac/econometrics
- https://notebooks.quantumstat.com/
- https://github.com/joosthub/PyTorchNLPBook
- https://github.com/mohammedterry/NLP_for_ML
- https://github.com/andkret/Cookbook
- https://github.com/eselkin/awesome-computational-neuroscience
- https://redwood.berkeley.edu/courses/vs265/
- https://cs230.stanford.edu/
- https://cs.stanford.edu/~ermon/cs228/index.html
- https://statsthinking21.github.io/statsthinking21-core-site/
- https://github.com/amit-sharma/causal-inference-tutorial/
- https://github.com/ybayle/awesome-deep-learning-music
- https://github.com/florent-leclercq/Bayes_InfoTheory
- https://github.com/physhik/Study-of-David-Mackay-s-book-
- https://github.com/ShuaiW/data-science-question-answer
- https://github.com/bt3gl/Book_on_Python_Algorithms_and_Data_Structure/
- https://github.com/gopala-kr/trending-repos
- https://github.com/rasbt/python-machine-learning-book1
- https://github.com/eriklindernoren/ML-From-Scratch
- https://github.com/CompPhysics/ComputationalPhysics
- https://github.com/ml4a/ml4a-guides
- https://github.com/khipu-ai/practicals-2019
- https://github.com/tensorflow/probability
- http://colah.github.io/
- https://christophm.github.io/interpretable-ml-book/
- http://web.math.ku.dk/~peters/elements.html
- https://www.dropbox.com/s/o4345krw428kyld/11283.pdf
- https://github.com/tugstugi/dl-colab-notebooks
- https://github.com/zaidalyafeai/Notebooks
- https://github.com/tensorflow/workshops
- http://www.inference.org.uk/itprnn/book.pdf
- https://www.biorxiv.org/content/biorxiv/early/2018/04/06/295964.full.pdf
- http://www.pnas.org/content/pnas/112/4/1036.full.pdf
- Equilibrium propagation https://www.frontiersin.org/articles/10.3389/fncom.2017.00024/full
- https://en.wikipedia.org/wiki/Free_energy_principle
- https://en.wikipedia.org/wiki/Variational_Bayesian_methods
- Helmholzt machine hinton http://www.gatsby.ucl.ac.uk/~dayan/papers/hm95.pdf
- Curiosity https://pathak22.github.io/noreward-rl/
- Chaos and fractals http://carlosreynoso.com.ar/archivos/peitgen.pdf
- Attention https://arxiv.org/abs/1706.03762
- World Models https://worldmodels.github.io
- https://github.com/whoafridi/Machine-Learning-Books/blob/master/book/Thoughtful%20Machine%20Learning%20with%20Python%20A%20Test-Driven%20Approach.pdf
- tensorflow/probability: Probabilistic reasoning and statistical analysis in TensorFlow
- Continuous attractor networks
- Hierarchical Temporal Memory
- Energy based models
- Markov Blanket, Friston
- Differential Plasticity
- Dilated convolutions
- Capsule networks
- AdaNet
- Evolutionary Alg
- Cellular Automata
- Neural Turing Machines
- https://github.com/dennybritz/deeplearning-papernotes
- CS50 on edX
- HackerRank's 30 days of code challenge
- Data Structures and Algorithms in Python by Michael T. Goodrich (book)
- Cracking the Coding Interview by Gayle Laakmann McDowell (book)
- Michael Nielson's deep learning textbook
- Universal Language Model Fine-tuning for Text Classification
- Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
- Deep Contextualized Word Representations
- An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
- Delayed Impact of Fair Machine Learning
- Taskonomy: Disentangling Task Transfer Learning
- Know What You Don’t Know: Unanswerable Questions for SQuAD
- Large Scale GAN Training for High Fidelity Natural Image Synthesis
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- https://www.cybrary.it/course/python/
- https://github.com/WillKoehrsen/machine-learning-project-walkthrough
- https://github.com/tensorflow/models/tree/master/research
- https://www.w3resource.com/python-exercises/
- https://github.com/google-research/google-research
- https://github.com/kailashahirwar/cheatsheets-ai
- https://web.stanford.edu/class/cs259d
- https://github.com/graykode/nlp-tutorial
- https://github.com/mrm8488/shared_colab_notebooks
-
Oxford Machine Learning, 2014-2015 Slides in .pdf, videos. Mathematical problem sets & practicals in Torch. By Nando de Freitas.
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NYU DS-GA-1003: Machine Learning and Computational Statistics, Spring 2016 Slides, notes, additional references to books and videos for some of the lectures.
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CMU 10-701/15-781 Machine Learning, Spring 2011 Lectures by Tom Mitchell. Slides, videos, additional readings and handouts.
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CMU 10-701/15-781 Machine Learning, 2015 Lectures by Alex Smola. Slides, high-quality videos, additional readings and handouts.
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Stanford CS229: Machine Learning A classic by Andrew NG. Video lectures (old but very good in terms of content!), useful notes & review materials + assignmets. Materials (except videos) from 2016 available here.
-
Columbia COMS 4771: Machine Learning & COMS 4772: Advanced Machine Learning Lecture notes in form of slides + related notes and homework assignments.
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Berkeley CS 189/289A: Introduction to Machine Learning, Spring 2017 Lecture notes and assigments.
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UBC CPSC 340: Machine Learning and Data Mining, 2012 Bachelor's level ML course by Nando de Freitas. Videos, slides and assignments.
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UBC CPSC 540: Machine Learning, 2013 MSc level course analogous to the one above by Nando de Freitas. Videos, slides and assignments.
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Duke STA561 COMPSCI571: Probabilistic Machine Learning, Fall 2015 Notes and readings + homeworks.
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CMU 10-715: Advanced Introduction to Machine Learning, Fall 2015 Video lectures by Alex Smola & Barnabas Poczos, slides and additional readings + homework.
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CMU 10-702/36-702: Statistical Machine Learning, Spring 2016 Lecture videos, notes and assignments by Larry Wasserman. Cource concentrated on theoretical foundations.
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Harvard CS281: Advanced Machine Learning, Fall 2013 Compiled resources on topics contained in the course - videos, papers, notes and assignments.
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John Hopkins University: Unsupervised Learning: From Big Data to Low-Dimensional Representations, 2017 Video lectures and book.
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Princeton COS511: Theoretical Machine Learning, Spring 2014 Lecture notes and readings.
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University of Washington EE512A: Advanced Inference in Graphical Models, Fall Quarter, 2014 Lecture videos & slides.
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Berkeley CS281a: Statistical Learning Theory Metacademy roadmap wit various materials on topics connected with the course.
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MIT 9.520/6.860: Statistical Learning Theory and Applications, Fall 2016 Readings & link to videos from Fall 2015 class.
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Stanford CS231n: Convolutional Neural Networks for Visual Recognition Video lectures, notes, papers and coding assignments in python.
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Stanford CS224d: Deep Learning for Natural Language Processing Video lectures, notes, papers and problem sets.
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Toronto CSC2523: Deep Learning in Computer Vision A lot of papers & some code connected with DL in CV.
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Berkeley Stat212B: Topics Course on Deep Learning, Spring 2016 Lecture slides and a lot of papers to read.
- Awesome Machine Learning
- Awesome courses - Machine Learning
- Awesome courses - Deep Learning
- Awesome Natual Language Processing
- All awesome lists
- Awesome math
- Oxford Statistics
- CMU 36-705 Intermediate Statistics by Larry Wasserman, advanced theoretical course
- NYU DS-GA 1002: Statistical and Mathematical Methods
- Stanford EE364a Convex Optimization I, 2016-17. Videos (older), textbook & slides
- Harvard CS109 Data Science
- MIT Introduction to Probability and Statistics, Spring 2014
- MIT Probabilistic Systems Analysis and Applied Probability, Fall 2010. Great course on probability - a slightly differnet version available on edX
- MIT Mathematics for Computer Science, Fall 2010
- Recommended Math books - various topics
- Tensorflow tutorials - Hvass Labs, github & youtube videos
- Deep Learning with TensorFlow - Big Data University, course & youtube videos
- Stanford CS20SI: Tensorflow for Deep Learning Research + Unofficial videos
- Effective Tensorflow
- ML & DL Tutorials Compilation
- Python Data Science tutorials
- Modern Pandas - 7 parts on pandas code
- Duke Computational Statistics in Python
- fast.ai Practical Deep Learning For Coders - part I and II