Building Machine Learning Solutions with TensorFlow.js 2
Machine learning and deep learning are powering some of the most groundbreaking applications of the current era. JavaScript was not traditionally considered the go-to language for machine...
Prerequisites: Basic JavaScript knowledge, familiarity with machine learning and deep learning concepts.
TensorFlow.js changes this paradigm by enabling inference directly in the browser — and even training new models or retraining pre-trained models locally, without sending any data over the network.
Real-world example: Companies like Airbnb use TensorFlow.js models on the client side to detect sensitive or personal information while users upload pictures, without that data ever leaving the device.
Sign in to read this course
A free account unlocks all 514 courses. 20 are readable without one.
What's inside
13 sections- 1 Table of Contents
- 2 Module 1 – Course Overview
- 3 Module 2 – Introduction
- 4 Module 3 – Setting up TensorFlow.js Environment
- 5 Module 4 – Understanding TensorFlow.js Core Concepts
- 6 Module 5 – Preparing Data for Machine Learning Model: Part 1
- 7 Module 6 – Preparing Data for Machine Learning Model: Part 2
- 8 Module 7 – Building, Training, and Evaluating Machine Learning Model
- 9 Module 8 – Saving and Loading Machine Learning Model
- 10 Module 9 – Predicting Using Trained Machine Learning Model
- 11 Module 10 – Using Pre-trained Models with TensorFlow.js
- 12 Module 11 – What's Next?
- 13 Architecture Diagrams
More Deep Learning & Neural Networks courses
View all 16Build, Train and Deploy Your First Neural Network with TensorFlow 2
The full ML workflow in TensorFlow 2 and Keras — build, train, monitor and deploy a neural network.
Deep Learning Frameworks and Model Implementation
Why frameworks matter and how to build, train and ship a production-ready deep-learning model.
Build a Generative AI Model
Build and train autoencoders and progress to GANs through the Globomantics business use case.
Advanced TensorFlow: Custom Training and Optimization
tensorflow · custom · optimization · deep · neural · networks · machine · data · science · loop · gradient · checklist · distributed · clipping · model · nested · components · correctness...
Build a Machine Learning Workflow with Keras TensorFlow 2.0
machine · workflow · keras · tensorflow · 2.0 · deep · neural · networks · data · science · models · layers · custom · model · reference · unsupervised · callbacks · convolutional · datas...
Build a Speech Recognition Model
ASR (_Automatic Speech Recognition_) is the technology that allows computers to convert spoken language into written text. At a fundamental level, ASR systems receive an audio signal — es...
Interested in this course?
Contact us to book it or get a custom training plan for your team.