imclab / neural-networks-and-deep-learning-ko

Translation of Neural Networks and Deep Learning by Michael Nielsen

Neural networks and Deep learning ko

This is a translation of Neural Networks and Deep Learning by Michael Nielsen. I started this project to learn more about deep learning. Also I wanted to share good study material to Korean students who also have a same interest.

Status

• Chapter 1: Using neural nets to recognize handwritten digits - in progress
• Perceptrons
• Sigmoid neurons
• The architecture of neural networks
• A simple network to classify handwritten digits
• Learning with gradient descent
• Implementing our network to classify digits
• Toward deep learning
• Chapter 2: How the backpropagation algorithm works
• Warm up: a fast matrix-based approach to computing the output from a neural network
• The two assumptions we need about the cost function
• The Hadamard product
• The four fundamental equations behind backpropagation
• Proof of the four fundamental equations (optional)
• The backpropagation algorithm
• The code for backpropagation
• In what sense is backpropagation a fast algorithm?
• Backpropagation: the big picture

Translator

• Taehoon Kim

Copyright

This work is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License. This means you're free to copy, share, and build on this book, but not to sell it.