Syllabus: Neural Networks and Deep Learning

The course Neural Networks and Deep Learning are based on a series of materials provided by Michael Nielsen. In this course, you will learn about the core concepts behind neural networks and deep learning.

First, please consider sending Michael Nielsen a thank you note for all his efforts by:


:white_small_square: Introduction

:white_small_square: Lesson 1. Using neural nets to recognize handwritten digits
:white_small_square::white_small_square: Lesson 1.1. Perceptrons
:white_small_square::white_small_square: Lesson 1.2 Sigmoid Neurons
:white_small_square::white_small_square: Lesson 1.3 The Architecture of Neural Networks
:white_small_square::white_small_square: Lesson 1.4 A Simple Network to Classify Handwritten Digits
:white_small_square::white_small_square: Lesson 1.5 Learning with gradient descent
:white_small_square::white_small_square: Lesson 1.6 Implementing our network to classify digits
:white_small_square::white_small_square: Lesson 1.7 Toward Deep Learning

:white_small_square: Appendix: Is there a simple algorithm for intelligence?
:white_small_square: Documentation - Getting Started
:white_small_square: FAQ