فهرست مطالب

• Deep Learning with JAX
• brief contents
• contents
• preface
• acknowledgments
• about this book
• about the author
• about the cover illustration
• Part 1
• Chapter 1: When and why to use JAX
• 1.1 Reasons to use JAX
• 1.2 How is JAX different from NumPy?
• 1.3 How is JAX different from TensorFlow and PyTorch?
• Chapter 2: Your first program in JAX
• 2.1 A toy ML problem: Classifying handwritten digits
• 2.2 An overview of a JAX deep learning project
• 2.3 Loading and preparing the dataset
• 2.4 A simple neural network in JAX
• 2.5 vmap: Auto-vectorizing calculations to work with batches
• 2.6 Autodiff: How to calculate gradients without knowing about derivatives
• 2.7 JIT: Compiling your code to make it faster
• 2.8 Saving and deploying the model
• 2.9 Pure functions and composable transformations: Why are they important?
• Part 2
• Chapter 3: Working with arrays
• 3.1 Image processing with NumPy arrays
• 3.2 Arrays in JAX
• 3.3 Differences from NumPy
• 3.4 High-level and low-level interfaces: jax.numpy and jax.lax
• Chapter 4: Calculating gradients
• 4.1 Different ways of getting derivatives
• 4.2 Calculating gradients with autodiff
• 4.3 Forward- and reverse-mode autodiff
• Chapter 5: Compiling your code
• 5.1 Using compilation
• 5.2 JIT internals
• 5.3 JIT limitations
• Chapter 6: Vectorizing your code
• 6.1 Different ways to vectorize a function
• 6.2 Controlling vmap() behavior
• 6.3 Real-life use cases for vmap()
• Chapter 7: Parallelizing your computations
• 7.1 Parallelizing computations with pmap()
• 7.2 Controlling pmap() behavior
• 7.3 Data-parallel neural network training example
• 7.4 Using multihost configurations
• Chapter 8: Using tensor sharding
• 8.1 Basics of tensor sharding
• 8.2 MLP with tensor sharding
• Chapter 9: Random numbers in JAX
• 9.1 Generating random data
• 9.2 Differences with NumPy
• 9.3 Generating random numbers in real-life applications
• Chapter 10: Working with pytrees
• 10.1 Representing complex data structures as pytrees
• 10.2 Functions for working with pytrees
• 10.3 Creating custom pytree nodes
• Part 3
• Chapter 11: Higher-level neural network libraries
• 11.1 MNIST image classification using an MLP
• 11.2 Image classification using a ResNet
• 11.3 Using the Hugging Face ecosystem
• Chapter 12: Other members of the JAX ecosystem
• 12.1 Deep learning ecosystem
• 12.2 Machine learning modules
• 12.3 JAX modules for other fields
• Appendix A: Installing JAX
• Appendix B: Using Google Colab
• Appendix C: Using Google Cloud TPUs
• Appendix D: Experimental parallelization
• Index