Prerequisites

• Linear Algebra [Book][Video]
• Probability and Information Theory [Book][Solution Manual]
• Numerical Computation [Book][Book]
• Calculus [Video]

Basic

• Neural Networks for Machine Learning by Geoffrey Hinton [Video]
• Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron [Book]
• Neural Networks and Deep Learning by Michael A Nielsen [Book]
• Stanford CS109 [Slide]
• Stanford CS229 [Slide]
• Stanford UFLDL [Web]

Intermediate

• Stanford CS231 [Slide]
• MIT Deep Learning [Video+Slide]
• Deep Learning by Google [Free Online Course]
• Deep Learning by Andrew Ng [Paid Online Course]
• Colah’s blog [Web]

Advance

• UC Berkeley Deep Reinforcement [Slide]
• Automatic Differentiation by David Barber [Slide]

Please ping me, if you find any dead links.