[Book] Deep Learning MIT


Chapter 1, Introduction.
Chapter 2, Linear Algebra.
Chapter 3, Probability and Information Theory.
Chapter 4, Numerical Computation.
Chapter 5, Machine Learning Basics.
Chapter 6, Deep Feedforward Networks.
Chapter 7, Regularization for Deep Learning.
Chapter 8, Optimization for Training Deep Models.
Chapter 9, Convolutional Networks.
Chapter 10, Sequence Modeling: Recurrent and Recursive Nets.
Chapter 11, Practical Methodology.
Chapter 12, Applications.
Chapter 15, Representation Learning.
Chapter 16, Structured Probabilistic Models for Deep Learning.
Chapter 18, Confronting the Partition Function.
Chapter 19, Approximate Inference.
Chapter 20, Deep Generative Models.




Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google+ photo

You are commenting using your Google+ account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )


Connecting to %s