Skip to content
Translation of Neural Networks and Deep Learning by Michael Nielsen
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Type Name Latest commit message Commit time
Failed to load latest commit information.

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.


  • 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



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.

You can’t perform that action at this time.