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Created November 13, 2017 15:55
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Recommended online materials for engineers looking to learn more about Deep Learning. I compiled this list from assorted recommendations from top ML engineers at Twitter, NVIDIA, Tesla, and others.
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deep-learning-syllabus.md

Introduction to Deep Learning - Online Materials

Linear Algebra

http://www.deeplearningbook.org/contents/linear_algebra.html

Probability Theory

http://www.deeplearningbook.org/contents/prob.html

Numeric Computation

http://www.deeplearningbook.org/contents/numerical.html

Matrix (n-dim array) math / software packages

Play around & familiarize yourself with numpy, matlab...

Basic Machine Learning

http://www.deeplearningbook.org/contents/ml.html

at the very least, understand:

  • Logistic Regression
  • Max Likelihood Estimation
  • training, validation and testing
  • basic notions of regularization

Extra Credit

General concepts of statistical pattern recognition

chapter 1: http://cs.du.edu/~mitchell/mario_books/Neural_Networks_for_Pattern_Recognition_-_Christopher_Bishop.pdf

Nice, high-level overview of modern neural nets & its application

http://neuralnetworksanddeeplearning.com/index.html

Additional Materials @ CS231n

Convolutional Neural Networks for Visual Recognition http://cs231n.stanford.edu/

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