intro.md

Hacker's approach (For the experimenters at heart)

1. Borgen's Learning how to code neural networks
2. Checkout Andrej Karpathy hackers guide
3. Matt Mazur's Backpropagation Example
4. In Practise
   a. Danile Nouri's Facial KeyPoint Detection
   b. Nouri's blog on listening to whale
5. Colah's (Theory Blogs)
   a. Understanding Convolutions
   b. Conv Nets a Modular Perspective
   c. Networks Manifold and Topology
   d. Neural Netwoks Types and Functional Programming

Courses (For those who want to dig deeper)

Introduction

• Good introduction to Basic Neural Networks and Autoencoders: Andrew Ng Video 1, Video 2, Notes

Neural Networks Courses

• Stanford CS231n Videos and Notes & Assignments
• Oxford Deep Learning: Nando De Freitas
• Udadity Course

Basics

• Stanford CS229: Machine Learning
• MIT OCW 6.041: Probability and Random Variables
• MIT Algorithms Basics, Analysis, Advanced

Advanced Courses

• Stanfrod CS 228: Probablistic Graphical Models
• Ecole Centrale Paris: Markov Random Fields
• EPFL Linear and Discrete Optimization
• Stanford Convex Optimization a and Convex Optimization b