d45: bunch of resources on AI (by Joshua_Dunigan)

Prepared by: Joshua_Dunigan


 

Basics

Programming

  • Learning how to program
  • Python
  • R

 

Mathematics

 

Statistics

 

Data Science

 

Machine Learning

 

Deep Learning

 

Cognitive Thinking

 

Neuroscience

 

Artificial Intelligence

 

Researchers and People to know

 

Textbooks/Papers

  • Bayesian Reasoning and Machine Learning – David Barber
  • Where Do Features Come From? Geoffrey Hinton
  • Modeling Documents With a Deep Boltzmann Machine – Geoffrey Hinton, Nitish Srivastava, and Ruslan Salakhutdinov
  • Distilling the Knowledge in a Neural Network – Geoffrey Hinton, Oriol Vinyalis, and Jeff Dean
  • Grammar as a Foreign Language – Hinton plus others
  • Information Science and Statistics – Christopher Bishop
  • Information Theory, Inference, and Learning Algorithms – David MacKay
  • An Introduction into Statistical Learning with Applications in R
  • Dropout: A Simple Way to Prevent Neural Networks from Overfitting – Toronto CS
  • Machine Learning – Peter Flach
  • Building Machine Learning Systems with Python – Willi Richert
  • To Recognize Shapes, First Learn to Generate Images – Hinton
  • Deep Learning – LeCun, Bengio, Hinton
  • A Fast Learning Algorithm for Deep Belief Nets – Hinton, Teh, Osindero
  • Speech Recognition with Deep Recurrent Neural Networks – Hinton, Mohammed, Graves
  • Reducing the Dimensionality of Data with Neural Networks – Hinton, Salakhuditinov
  • Superintelligence Paths Dangers Stragies – Bostrom

Other

 

References

 

 

 

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