TensorFlow.js in 3 Hours [Video]
This is the code repository for TensorFlow.js in 3 Hours [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
About the Video Course
In this course, you will go through the process of learning how to use Tensorflow.js in a variety of the most popular deep learning tasks and use it in your own web browser.
You will start by learning how to classify images using commonly used Convolutional Neural Networks. And to get up and running fast you will use a pre-trained model to do that. Then you will jump into exploring yet another popular deep learning architecture called Long-Short Term Memory Recurrent Neural Network. This time to classify text typed by a user in a real-time. You will discover how to work with audio data using a specific type of CNN. You will then jump into methods to improve the results of our models by firstly looking at transfer learning. Here you will improve the performance of your model quickly by using a pre-trained model as a base and perform short, focused training.
By the end of this course, you will have the skills to use Tensorflow.js and train your own personal models using only a web browser.
What You Will Learn
- Get up and running with Tensorflow.js quickly by working on pre-trained Deep Learning models
- Explore effective ways to solve common Machine Learning problems
- Get great results by using pre-trained models while saving time
- Discover how to use transfer learning to quickly improve your model’s performance
- Train your own model to become the best choice to use by increasing its efficiency
Instructions and Navigation
Assumed Knowledge
To fully benefit from the coverage included in this course, you will need:
To fully benefit from the coverage included in this course, you will need:
Some very basic programming knowledge
I will cover the Javascript Basics
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
Technical Requirements
This course has the following software requirements:
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
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