Goals

The MOST in-depth look at neural network theory for machine learning, with both pure Python and Tensorflow code

Focus

What you'll learn

Learn how Deep Learning REALLY works (not just some diagrams and magical black box code)

Learn how a neural network is built from basic building blocks (the neuron)

Code a neural network from scratch in Python and numpy

Code a neural network using Google's TensorFlow

Describe different types of neural networks and the different types of problems they are used for

Derive the backpropagation rule from first principles

Create a neural network with an output that has K > 2 classes using softmax

Describe the various terms related to neural networks, such as "activation", "backpropagation" and "feedforward"

Install TensorFlow

Program

Welcome

Review

Preliminaries: From Neurons to Neural Networks

Classifying more than 2 things at a time

Training a neural network

Practical Machine Learning

TensorFlow, exercises, practice, and what to learn next

Project: Facial Expression Recognition

Backpropagation Supplementary Lectures

Higher-Level Discussion

Invited Persons

Benefits

Papers