**Basic Neural Networks**

The first link gives some background on auto encoders and the second link is to an online text extract providing an overview of several additional basic DNN architectures. The third link is to a set of online tutorials from Stanford. The fourth link is to a preprint of a deep learning textbook written by Yoshua Bengio, professor at the Department of Computer Science and Operations Research at Université de Montréal, and Ian Goodfellow. The fifth link is to Theano (a python library for GPU machines) code for deep learning. The sixth link is a tutorial video by Geoffrey Hinton, Yann LeCun, and Yoshua Bengio, and the seventh link is for the slides for the NIPS tutorial. A bunch of tools and tutorials follow.

Basic types of deep neural network architectures

Stanford Deep Learning Tutorial

http://www.deeplearningbook.org

http://deeplearning.net/tutorial/

Deep Learning in a Nutshell – what it is, how it works, why care?

The mostly complete chart of Neural Networks, explained

###### CNNs

Intuitively Understanding Convolutions for Deep Learning

How to Design a Convolutional Neural Network

###### RNNs

Recurrent Neural Networks and LSTM

###### Reinforcement Learning

Sutton, Barto — Reinforcement Learning: An Introduction

Learning Reinforcement Learning (with Code, Exercises and Solutions)

Deep Learning in a Nutshell: Reinforcement Learning

MIT Technology Review: 10 Breakthrough Technologies — Reinforcement Learning

Reinforcement learning tutorial with TensorFlow

###### Tools

How to build your own Neural Network from scratch in Python

Dopamine: Reinforcement Learning Research Framework built atop TensorFlow

TensorBoard: Visualizing Learning

TensorFlow Fold: Deep Learning With Dynamic Computation Graphs

TensorFlow Probability: Library for Probabilistic Reasoning and Statistical Analysis in TensorFlow

The Matrix Calculus You Need For Deep Learning

Where did the Least Square Come From?

Best Practices/Parameter Setting