JAX + GPU AI Crash Course

This book aspires young graduates and programmers to become AI engineers and enter the world of artificial intelligence by combining powerful Python programming with artificial intelligence. Beginning with the fundamentals of Python programming, the book gradually progresses to machine learning, where readers learn to implement Python in developing predictive models.

The book provides a clear and accessible explanation of machine learning, incorporating practical examples and exercises that strengthen understanding. We go deep into deep learning, another vital component of AI. Readers gain a thorough understanding of how Python's frameworks and libraries can be used to create sophisticated neural networks and algorithms, which are required for tasks such as image and speech recognition. Natural Language Processing is also covered in the book, with fundamental concepts and techniques for interpreting and generating human-like language covered.

The book's focus on computer vision and reinforcement learning is distinctive, presenting these cutting-edge AI fields in an approachable manner. Readers will learn how to use Python's intuitive programming paradigm to create systems that interpret visual data and make intelligent decisions based on environmental interactions. The book focuses on ethical AI development and responsible programming, emphasizing the importance of developing AI that is fair, transparent, and accountable.

Each chapter is designed to improve learning by including practical examples, case studies, and exercises that provide hands-on experience. This book is an excellent starting point for anyone interested in becoming an AI engineer, providing the necessary foundational knowledge and skills to delve into the fascinating world of artificial intelligence.

Key Learnings

• Explore Python basics and AI integration for real-world application and career advancement.
• Experience the power of Python in AI with practical machine learning techniques.
• Practice Python's deep learning tools for innovative AI solution development.
• Dive into NLP with Python to revolutionize data interpretation and communication strategies.
• Simple yet practical understanding of reinforcement learning for strategic AI decision making.
• Uncover ethical AI development and frameworks, and concepts of responsible and trustworthy AI.
• Harness Python's capabilities for creating AI applications with a focus on fairness and bias.

Table of Content

1. Introduction to Artificial Intelligence
2. Python for AI
3. Data as Fuel for AI
4. Machine Learning Foundation
5. Essentials of Deep Learning
6. NLP and Computer Vision
7. Hands-on Reinforcement Learning
8. Ethics to AI

This is the practical, solution-oriented book for every data scientists, machine learning engineers, and AI engineers to utilize the most of Google JAX for efficient and advanced machine learning. It covers essential tasks, troubleshooting scenarios, and optimization techniques to address common challenges encountered while working with JAX across machine learning and numerical computing projects.The book starts with the move from NumPy to JAX. It introduces the best ways to speed up computations, handle data types, generate random numbers, and perform in-place operations. It then shows you how to use profiling techniques to monitor computation time and device memory, helping you to optimize training and performance. The debugging section provides clear and effective strategies for resolving common runtime issues, including shape mismatches, NaNs, and control flow errors. The book goes on to show you how to master Pytrees for data manipulation, integrate external functions through the Foreign Function Interface (FFI), and utilize advanced serialization and type promotion techniques for stable computations.If you want to optimize training processes, this book has you covered. It includes recipes for efficient data loading, building custom neural networks, implementing mixed precision, and tracking experiments with Penzai. You'll learn how to visualize model performance and monitor metrics to assess training progress effectively. The recipes in this book tackle real-world scenarios and give users the power to fix issues and fine-tune models quickly.

Key Learnings

• Get your calculations done faster by moving from NumPy to JAX's optimized framework.
• Make your training pipelines more efficient by profiling how long things take and how much memory they use.
• Use debugging techniques to fix runtime issues like shape mismatches and numerical instability.
• Get to grips with Pytrees for managing complex, nested data structures across various machine learning tasks.
• Use JAX's Foreign Function Interface (FFI) to bring in external functions and give your computational capabilities a boost.
• Take advantage of mixed-precision training to speed up neural network computations without sacrificing model accuracy.
• Keep your experiments on track with Penzai. This lets you reproduce results and monitor key metrics.
• Use advanced visualization techniques, like confusion matrices and learning curves, to make model evaluation more effective.
• Create your own neural networks and optimizers directly in JAX so you have full control of the architecture.
• Use serialization techniques to save, load, and transfer models and training checkpoints efficiently.

Table of Content

1. Transition NumPy to JAX
2. Profiling Computation and Device Memory
3. Debugging Runtime Values and Errors
4. Mastering Pytrees for Data Structures
5. Exporting and Serialization
6. Type Promotion Semantics and Mixed Precision
7. Integrating Foreign Functions (FFI)
8. Training Neural Networks with JAX

If you're a Python pro looking to get the most out of your code with GPUs, then Practical GPU Programming is the right book for you. This book will walk you through the basics of GPU architectures, show you hands-on parallel programming techniques, and give you the know-how to confidently speed up real workloads in data processing, analytics, and engineering.

The first thing you'll do is set up the environment, install CUDA, and get a handle on using Python libraries like PyCUDA and CuPy. You'll then dive into memory management, kernel execution, and parallel patterns like reductions and histogram computations. Then, we'll dive into sorting and search techniques, but with a focus on how GPU acceleration transforms business data processing. We'll also put a strong emphasis on linear algebra to show you how to supercharge classic vector and matrix operations with cuBLAS and CuPy. Plus, with batched computations, efficient broadcasting, custom kernels, and mixed-library workflows, you can tackle both standard and advanced problems with ease.

Throughout, we evaluate numerical accuracy and performance side by side, so you can understand both the strengths and limitations of GPU-based solutions. The book covers nearly every essential skill and modern toolkit for practical GPU programming, but it's not going to turn you into a master overnight.

Key Learnings

• Boost processing speed and efficiency for data-intensive tasks.
• Use CuPy and PyCUDA to write and execute custom CUDA kernels.
• Maximize GPU occupancy and throughput efficiency by using optimal thread block and grid configuration.
• Reduce global memory bottlenecks in kernels by using shared memory and coalesced access patterns.
• Perform dynamic kernel compilation to ensure tailored performance.
• Use CuPy to carry out custom, high-speed elementwise GPU operations and expressions.
• Implement bitonic and radix sort algorithms for large or batch integer datasets.
• Execute parallel linear search kernels to detect patterns rapidly.
• Scale matrix operations using Batched GEMM and high-level cuBLAS routines.

Table of Content

1. Introduction to GPU Fundamentals
2. Setting up GPU Programming Environment
3. Basic Data Transfers and Memory Types
4. Simple Parallel Patterns
5. Introduction to Kernel Optimization
6. Working with PyCUDA and CuPy Features
7. Practical Sorting and Search
8. Linear Algebra Essentials on GPU