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:
Run existing TensorFlow.js models
Deploy ML models to web browsers
Step 1: Quick introduction to machine learning in the browser.
Step 2: Dive deeper into Deep Learning
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.
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. 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. To experiment with common use cases, you can start exploring convolutional neural networks using the mnist example, try transfer learning using the mnist-transfer-cnn example, or see how recurrent neural networks are structured with the addition-rnn example.
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. 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.