Tensorflow.js Explained
This text is a reference for learning and mastering Tensorflow (applied perspective). In these pages, we provide links to official code and documentation, discuss how the Tensorflow.js library works, present code examples in editable sandboxes with explanations.
Copyright © 2023 Benjamin Kenwright.
The examples for this book were developed by Benjamin Kenwright and is licensed under the MIT license.
The cover art was created by Benjamin Kenwright visualizing the synergy between intelligence and processor technologies. Important to see the bigger picture around intelligent systems (avoid only learning APIs and/or the programming languages), but the what and why they work the way they do. In fact, we'd recommend you read around the subject, such as, the history and related concepts, as it's inspiring to see how far we've come in such a small space of time (and where we'll go tomorrow).
Special thanks to reviewers, colleagues and friends for input and discussion while developing the initial versions of the text.
Contents
Chapter 1: Introduction
- Overview of TensorFlow.js
- Advantages and use cases of TensorFlow.js
- Comparison with other deep learning frameworks
Chapter 2: Getting Started with TensorFlow.js
- Installing TensorFlow.js
- Setting up the development environment
- Introduction to JavaScript and Node.js
Chapter 3: TensorFlow.js Basics
- Tensor operations and computations
- Creating and manipulating tensors
- Introduction to graph computation
Chapter 4: Loading and Saving Models
Chapter 5: Building Neural Networks with TensorFlow.js
- Introduction to neural networks
- Creating a basic neural network architecture
- Configuring layers and activation functions
Chapter 6: Training Models with TensorFlow.js
- Defining loss functions and optimizers
- Training process and techniques
- Evaluating and improving model performance
Chapter 7: Transfer Learning with TensorFlow.js
- Understanding transfer learning
- Adapting pre-trained models for new tasks
- Fine-tuning and feature extraction
Chapter 8: Recurrent Neural Networks with TensorFlow.js
- Introduction to recurrent neural networks (RNNs)
- Implementing RNNs in TensorFlow.js
- Applications of RNNs in sequence modeling
Chapter 9: Convolutional Neural Networks with TensorFlow.js
- Convolutional layers and filters
- Building image classification models
- CNN architectures and advanced techniques
Chapter 10: Working with Images and Videos
- Image data preprocessing and augmentation
- Object detection and localization
- Video analysis and processing
Chapter 11: Natural Language Processing with TensorFlow.js
- Text data preprocessing and tokenization
- Word embeddings and text classification
- Language generation and sentiment analysis