Bringing AI directly into the browser unlocks new possibilities for privacy, offline usage, and instant feedback. In this deep dive, we’ll explore:

1. Getting Started with TensorFlow.js
- Installation and converting pre-trained models (from TensorFlow or Keras) to the browser format.
- Understanding the tf.Model and tf.Tensor abstractions.

2. Building a Simple Intent Classifier
- Preparing text data and using the Tokenizer and padSequences utilities.
- Defining a compact neural network architecture suitable for real-time inference.

3. Training & Transfer Learning
- Fine-tuning an existing model in the browser vs. pre-training on your machine.
- Capturing user feedback to retrain and improve accuracy on the fly.

4. Integrating with a Chat UI
- Managing state with React or vanilla JavaScript.
- Streaming responses and handling fallback to fallback to server-side inference if needed.

5. Performance & Memory
- Monitoring GPU vs. CPU backends in tf.js.
- Strategies for model quantization and pruning to reduce bundle size.

With code examples and live demos, you’ll learn to ship AI features that delight users without ever leaving the browser.