![](https://d3qjle651ffvx1.cloudfront.net/brioche/chapter/intro-neural-networks-MS8bJL.png)
5.1 Introduction to Neural Networks
Delve into the inner machinery of neural networks to discover how these flexible learning tools actually work.
Neural Networks
The Computer Vision Problem
The Folly of Computer Programming
Can Computers Learn?
The Decision Box
Activation Arithmetic
Decision Boundaries
Building an XOR Gate
Classification
Sigmoid Neurons
Training a Single Neuron
Hidden Layers
Curve Fitting
Universal Approximator
A Shape-Recognizing Network
Course description
Artificial neural networks learn by detecting patterns in huge amounts of information. Much like your own brain, artificial neural nets are flexible, data-processing machines that make predictions and decisions. In fact, the best ones outperform humans at tasks like chess and cancer diagnoses. In this course, you'll dissect the internal machinery of artificial neural nets through hands-on experimentation, not hairy mathematics. You'll develop intuition about the kinds of problems they are suited to solve, and by the end you’ll be ready to dive into the algorithms, or build one for yourself.
Topics covered
- Artificial Intelligence
- Classification
- Backpropagation
- Logic Gates
- Convolutional Networks
- Gradient Descent
- Computer Vision
- Activation Functions
- Universal Approximation
Prerequisites and next steps
A basic proficiency with algebra will help you understand this course. Remembering how to get the slope from an equation of a line is enough. Some basic knowledge of logic, like what **AND** and **OR** mean would also be useful. You don't need to know how to code to learn a lot from this course.
Up next
5.2 Quantum Computing
Solve hard problems by computing with quantum mechanics.
Jump ahead