Bootcamp
Study group
Collaborate with peers in your dedicated #study-group channel.
Code reviews
Submit projects for review using the /review command in your #code-reviews channel
Join 8494 other students
Log in to get
Subscribe to access to this course and ALL other courses. You get a 30-day money-back guarantee, no questions asked.
Subscription includes
Course level: Intermediate
A fun and practical Brain.js tutorial. This 19-part course teaches you how to build neural networks in JavaScript through interactive Scrimba tutorials.
This course contains 21 interactive scrims
Neural Network basics
How Neural Nets learn
Propagation
Structure
Layers
Objects
Normalization
Predicting steps
Recurrent Neural Net
Sentiment analysis
Before taking this course, you should have a basic understanding of JavaScript. Here’s our suggested resource to get you up to speed.
This course gives you a practical introduction to Brain.js, a popular JavaScript library for building neural networks in the browser and in Node.js. And since this is Scrimba, you'll be able to interact with the neural networks whenever you want. Simply pause the screencast, edit the code and run the network with your own changes applied. Learning machine learning has never been as interactive as this!
What you'll learn
By the end of the course, you'll be able to solve a range of different problems using neural networks. The lectures does not dwell with much theory, but rather on how to code the networks. That means the course is suitable for anybody who knows JavaScript.
Good luck, and welcome to the exciting world of neural networks!
Learning alone can be lonely. Click here to join our Discord server and connect with other Scrimba learners!
to the QNeural networks are a specific set of algorithms within machine learning. They are inspired by biological neural networks and the current so-called deep neural networks and have proven to work quite well.
Neural networks are biologically-inspired programming concept which enables a computer to learn from observational data. Deep learning is a set of techniques for learning in neural networks.
In short, neural networks can be used for solving business problems such as forecasting, customer research, data validation, and risk management. A more fun use could be to teach a neural network to play Mario cart.
Ability to learn and model non-linear and complex relationships, which is really important because in real-life, many of the relationships between inputs and outputs are non-linear as well as complex.
