This is the code repository for Deep Learning Projects with JavaScript [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
Getting started with Deep Learning might be overwhelming with so much options to choose from, so you might wondering: where to start? Which tools to choose and how to actually set them up? The good news is that you already have the key tool in front of you: your web browser with a powerful Javascript engine inside. And when you add TensorFlow.js library to this combo, you can use Deep Learning methods using Javascript in no time. In this course you will through the process of getting started with Tensorflow.js to detect emotions with a lot of different types of data.
You will start by learning how to build a deep learning tool to judge if a piece of text is positive or negative. Since you will want a tangible results quickly, you will use a pre trained model to do that and include it into your own web application. You will move to learn how to detect human emotions based only on their pictures and voice using pre trained models as well. Towards the end you will learn how to modify a pre-trained model to perform transfer learning and then how to train the emotional detector from scratch using your own data.
By the end of this course you will know how to use Deep Learning models and train your own models from the ground up using JavaScript and TensorFlow.js library.
- Get started with Deep Learning quickly, without installing anything
- Use Deep Learning methods in practice on realistic datasets
- Get results fast using pre-trained models
- Improve your results using transfer learning
- Learn when it's a good idea to train your own model from scratch and what do you need to know to do that correctly
To fully benefit from the coverage included in this course, you will need:
Working client-side (web browser) Javascript knowledge
The basics of Machine Learning - the main ideas behind training, validating and testing ML models
Basic shell skills - how to run a simple command from Terminal
This course has the following software requirements:
Google Chrome, minimum version: 52
Python 3.6 (https://www.python.org/downloads/)
A code editor, author used Atom in the course
This course has been tested on the following system configuration:
OS: macOS High Sierra
Processor: 1,3 GHz Intel Core 5
Memory: 4 GB
Storage: 121 GB