Here are the resources I found useful: ========================================== Advices from Open AI, Facebook AI leaders Courses You MUST Take: Machine Lea

Here are the resources I found useful: ========================================== Advices from Open AI, Facebook AI leaders Courses You MUST Take: Machine Learning by Andrew Ng (https://www.coursera.org/learn/machine-learning https://www.coursera.org/learn/machine-learning) /// Class notes: (http://holehouse.org/mlclass/index.html http://holehouse.org/mlclass/index.html) Yaser Abu-Mostafa’s Machine Learning course which focuses much more on theory than the Coursera class but it is still relevant for beginners. (https://work.caltech.edu/telecourse.html https://work.caltech.edu/telecourse.html) Neural Networks and Deep Learning (Recommended by Google Brain Team) (http://neuralnetworksanddeeplearning.com/ http://neuralnetworksanddeeplearning.com/) Probabilistic Graphical Models (https://www.coursera.org/learn/probabilistic-graphical-model... https://www.coursera.org/learn/probabilistic-graphical-model...) Computational Neuroscience (https://www.coursera.org/learn/computational-neuroscience https://www.coursera.org/learn/computational-neuroscience) Statistical Machine Learning (http://www.stat.cmu.edu/~larry/=sml/ http://www.stat.cmu.edu/~larry/=sml/) From Open AI CEO Greg Brockman on Quora Deep Learning Book (http://www.deeplearningbook.org/ http://www.deeplearningbook.org/) ( Also Recommended by Google Brain Team ) It contains essentially all the concepts and intuition needed for deep learning engineering (except reinforcement learning). by Greg 2. If you’d like to take courses: Linear Algebra — Stephen Boyd’s EE263 (Stanford) (http://ee263.stanford.edu/ http://ee263.stanford.edu/) or Linear Algebra (MIT) (http://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebr... http://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebr...) Neural Networks for Machine Learning — Geoff Hinton (Coursera) https://www.coursera.org/learn/neural-networks https://www.coursera.org/learn/neural-networks Neural Nets — Andrej Karpathy’s CS231N (Stanford) http://cs231n.stanford.edu/ http://cs231n.stanford.edu/ Advanced Robotics (the MDP / optimal control lectures) — Pieter Abbeel’s CS287 (Berkeley) https://people.eecs.berkeley.edu/~pabbeel/cs287-fa11/ https://people.eecs.berkeley.edu/~pabbeel/cs287-fa11/ Deep RL — John Schulman’s CS294–112 (Berkeley) http://rll.berkeley.edu/deeprlcourse/ http://rll.berkeley.edu/deeprlcourse/