Deep Learning for the Web using TensorFlow.js

This course will teach you how to use Machine Learning in JavaScript applications and websites. Deep Learning and Neural Networks have seen a lot of success in recent years, but they still seem intimidating to many. Our goal is to make it as simple as possible without having to have a math degree to participate.

Get hands-on experience with deep learning using JavaScript and expand your knowledge of building, training, and running machine learning models in the browser using the latest version of TensorFlow.js.

What you will learn:

Introduction to Deep Learning and Neural Networks
Introduction to TensorFlow.js
Tensors: uses and types
Models: saving and loading a model
UI: creating the UI; visualizing predictions
Classifiers: binary and multi-class
Adding a toxicity model classifier to a chat app for moderation
Preparing the data, designing, training, and testing models
Extending pre-trained models
Working with images: convolutions and image classification
Adding automatic image captioning to a web app using MobileNet
Deployment and performance: bundle optimizations
Bonus: Natural Language Processing and AI Assistants

Who is the target audience?
Anyone who wants to integrate machine learning into their apps or websites using JavaScript. No previous experience with machine learning is necessary.

Requirements:
You should be comfortable with JavaScript and have at least basic knowledge of HTML and CSS.

Gerard Sans

Google Developer Expert; former AWS Amplify team

Gerard Sans is a Computer Science Engineer and Google Developer Expert in AI, Cloud, and Angular. Formerly an AWS Amplify Developer Advocate, he now leads Axiom Professional Trainings and organises the NextAI London community. Passionate about the future of AI, Gerard enjoys sharing his knowledge through talks, workshops, and mentoring.

Deep Learning for the Web using TensorFlow.js

Gerard Sans

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