Image care of https://towardsdatascience.com/the-mostly-complete-chart-of-neural-networks-explained-3fb6f2367464

Learning Resources for Artificial Intelligence

• Math
• Traditional AI
• Machine Learning
• Deep Learning
• GPU Enabled Ubuntu

Math

• Linear Algebra
  • Essence of Linear Algebra by 3blue1brown: https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab
  • Gilbert Strang’s famous intro to Linear Algebra: https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/
• Essence of Calculus: https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr
• Probability:
  • Jaynes : https://www.amazon.com/Probability-Theory-Science-T-Jaynes/dp/0521592712
  • Rethinking Probability : http://xcelab.net/rm/statistical-rethinking/
• Differential Equations: https://ocw.mit.edu/resources/res-18-009-learn-differential-equations-up-close-with-gilbert-strang-and-cleve-moler-fall-2015/index.htm
• Real Analysis: https://www.youtube.com/watch?v=sqEyWLGvvdw
• Seeing Theory: http://students.brown.edu/seeing-theory/

Traditional AI

These topics fall outside of ‘Machine Learning’ or ‘Deep Learning’

• AI:  A modern approach
  • The classic textbook and the 4th most cited book in the 21st century.
  • The cheap copy of the book : https://www.amazon.com/Artificial-Intelligence-Approach-Stuart-Russell/dp/9332543518/
  • Exercises and Notebooks :https://github.com/aimacode
• Game AI
  • AlphaBeta demo from Berkeley – https://www.youtube.com/watch?v=xBXHtz4Gbdo
  • Tic Tac Toe with MiniMax – https://medium.freecodecamp.com/how-to-make-your-tic-tac-toe-game-unbeatable-by-using-the-minimax-algorithm-9d690bad4b37#.sr2qz5bhq
  • AlphaBeta Pruning – https://www.youtube.com/watch?v=xBXHtz4Gbdo
• Search AI
  • Heuristics (l8 and l9) – https://heuristicswiki.wikispaces.com/
  • Sebastian Thrun Q&A – https://www.youtube.com/watch?v=AijSw9Q3oas
  • Search Heuristic (l7 – Pacman) – http://theory.stanford.edu/~amitp/GameProgramming/Heuristics.html
• Logic
  • Description Logics – http://www.cs.ox.ac.uk/people/ian.horrocks/Publications/download/2007/BaHS07a.pdf
  • Knowledge Representation & Classical Logic – http://www.cs.utexas.edu/~vl/papers/Ch1.pdf
  • First-Order Logic – https://formal.iti.kit.edu/~beckert/teaching/Einfuehrung-KI-WS0304/08FirstOrderLogic.pdf
  • Overview of AI Planning – https://users.ics.aalto.fi/rintanen/planning.html
• Bayes Nets
  • https://www.youtube.com/playlist?list=PLjLGYJqya5UiWa1bNPAAX8oOQ1NCUbvB8
• Hidden Markov Models
  • HMM for DNA Analysis – http://www.lancs.ac.uk/~nemeth/Hidden%20Markov%20Models%20with%20Applications%20to%20DNA%20Sequence%20Analysis.pdf
  • HMM for Speech Recognition – http://www.cslu.ogi.edu/~zak/cs506-lvr/mjfg_NOW.pdf
  • HMM for Bioinformatics – http://www.cs.ubbcluj.ro/~csatol/mach_learn/bemutato/Mate_Korosi_HMMpres.pdf

Machine Learning

Come back to this, include things like SVM, k-NN, Naive Bayes, Random Forests, Linear Regression, LASSO, Decision Trees, Feature Learning, PCA .

• A really good look at linear regression in R : https://rpubs.com/ronniekolodziej/156135 
• R for data science : http://r4ds.had.co.nz/
• Applied Predictive Modelling
  • Book https://www.amazon.com/dp/1461468485 
  • Labs http://topepo.github.io/caret/index.html

Deep Learning

( Incomplete, just filling this in so I dont lose the RNN links )

• Recurrent Neural Networks
  • RNN in 100 lines of python https://gist.github.com/karpathy/d4dee566867f8291f086
  • http://karpathy.github.io/2015/05/21/rnn-effectiveness/
  • http://colah.github.io/posts/2015-08-Understanding-LSTMs/
  • For Speech Recognition https://svds.com/tensorflow-rnn-tutorial/
• CNN
  • https://www.youtube.com/watch?v=AQirPKrAyDg
• Reinforcement Learning
  • Anything from Andrej Karpathy is worth reading – http://karpathy.github.io/2016/05/31/rl/
  • Policy Gradients: http://pemami4911.github.io/blog/2016/08/21/ddpg-rl.html
  • Notebooks and Exercises: https://github.com/dennybritz/reinforcement-learning
• NLP
  • Stanford taught by Richard Socher https://www.youtube.com/watch?v=Qy0oEkCZkBI&list=PLlJy-eBtNFt4CSVWYqscHDdP58M3zFHIG&index=1
• Speech Recognition
  • Feature Extraction : http://www.ijcsmc.com/docs/papers/March2015/V4I3201545.pdf
  • python_speech_features: https://github.com/jameslyons/python_speech_features
  • Acoustic Model Features: https://pdfs.semanticscholar.org/a566/cd4a8623d661a4931814d9dffc72ecbf63c4.pdf
  • Ceptral Analysis : http://iitg.vlab.co.in/?sub=59&brch=164&sim=615&cnt=1
  • MFCC Tutorial : http://practicalcryptography.com/miscellaneous/machine-learning/guide-mel-frequency-cepstral-coefficients-mfccs/
  • Connectionist Temporal Classification http://machinelearning.wustl.edu/mlpapers/paper_files/icml2006_GravesFGS06.pdf
  • Audio Analysis with TensorFlow: https://medium.com/towards-data-science/audio-processing-in-tensorflow-208f1a4103aa
  • https://people.xiph.org/~jm/demo/rnnoise/

GPU enabled Ubuntu

After botching the install 4 or 5 times I finally found something that worked.

• Install Ubuntu LTS fresh
• Install NVIDIA drivers FROM THE UBUNTU REPOSITORY ( Usually named “Additional Drivers” ) .
• Install the CUDA libraries from NVIDIA , https://developer.nvidia.com/cuda-downloads. Choose runfile(local).  When prompted, DO NOT LET NVIDIA OVERWRITE THE DISPLAY DRIVER.
• Install cudacdnn from https://developer.nvidia.com/cudnn  , you have to register unfortunately.
• Install conda from https://www.continuum.io/downloads
• pip install keras theano tensorflow-gpu
• To force Tensorflow into using a specific device

• with tf.device('gpu:0'):
      # train your model