Before starting on the learning materials below, you should:
Be familiar with using the command line to run Node.js scripts
This curriculum is for people who want to:
Deploy ML models to web browsers
Step 1: Get introduced to machine learning in the browser
Go from zero to hero with web ML using TensorFlow.js. Learn how to create next generation web apps that can run client side and be used on almost any device.
Learn how to load and use one of the TensorFlow.js pre-trained models (COCO-SSD) and use it to recognize common objects it's been trained on.
Step 2: Dive deeper into Deep Learning
To get a deeper understanding of how neural networks work, and a broader understanding of how to apply them to different problems, we have two books available.
This book will demonstrate how to use a wide variety of neural network architectures, such as Convolutional Neural Networks, Recurrent Neural Networks, and advanced training paradigms such as reinforcement learning. It also provides clear explanations of what is actually happening with the neural network in the training process.
A hands-on end-to-end approach to TensorFlow.js fundamentals for a broad technical audience. Once you finish this book, you'll know how to build and deploy production-ready deep learning systems with TensorFlow.js.
Step 3: Practice with examples using TensorFlow.js
Practice makes perfect, and getting hands on experience is the best way to lock in the concepts. Check out the TensorFlow.js codelabs to further your knowledge with these step by step guides for common use cases:
Handwritten digit recognition with Convolutional Neural Networks
Retrain a comment spam detection model to handle custom edge cases
With your knowledge of neural networks, you can more easily explore the open sourced examples created by the TensorFlow team. They are all available on GitHub, so you can delve into the code and see how they work.
A repository on GitHub that contains a set of examples implemented in TensorFlow.js. Each example directory is standalone so the directory can be copied to another project.
The TensorFlow tutorials are written as Jupyter notebooks and run directly in Google Colab—a hosted notebook environment that requires no setup. Click the Run in Google Colab button.
Step 4: Make something new!
Once you've tested your knowledge, and practiced with some of the TensorFlow.js examples, you should be ready to start developing your own projects. Take a look at our pretrained models, and start building an app in minutes. Or you can train your own model using data you've collected, or by using public datasets. Kaggle and Google Dataset Search are great places to find open datasets for training your model.
If you are looking for inspiration, check out our Made With TensorFlow.js show and tell episodes from people all around the world who have used TensorFlow.js in their applications.
You can also see the latest contributions from the community by searching for the #MadeWithTFJS hashtag on social media.