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

Unlock the Power of Parallel Python with Dask: A Perfect Learning Guide for Aspiring Data Scientists

Dask has revolutionized parallel computing for Python, empowering data scientists to accelerate their workflows. This comprehensive guide unravels the intricacies of Dask to help you harness its capabilities for machine learning and data analysis.

Across 10 chapters, you'll master Dask's fundamentals, architecture, and integration with Python's scientific computing ecosystem. Step-by-step tutorials demonstrate parallel mapping, task scheduling, and leveraging Dask arrays for NumPy workloads. You'll discover how Dask seamlessly scales Pandas, Scikit-Learn, PyTorch, and other libraries for large datasets.

Dedicated chapters explore scaling regression, classification, hyperparameter tuning, feature engineering, and more with clear examples. You'll also learn to tap into the power of GPUs with Dask, RAPIDS, and Google JAX for orders of magnitude speedups.

This book places special emphasis on practical use cases related to scalability and distributed computing. You'll learn Dask patterns for cluster computing, managing resources efficiently, and robust data pipelines. The advanced chapters on DaskML and deep learning showcase how to build scalable models with PyTorch and TensorFlow.

With this book, you'll gain practical skills to:

• Accelerate Python workloads with parallel mapping and task scheduling
• Speed up NumPy, Pandas, Scikit-Learn, PyTorch, and other libraries
• Build scalable machine learning pipelines for large datasets
• Leverage GPUs efficiently via Dask, RAPIDS and JAX
• Manage Dask clusters and workflows for distributed computing
• Streamline deep learning models with DaskML and DL frameworks

Packed with hands-on examples and expert insights, this book provides the complete toolkit to harness Dask's capabilities. It will empower Python programmers, data scientists, and machine learning engineers to achieve faster workflows and operationalize parallel computing.

Table of Content

1. Introduction to Dask
2. Dask Fundamentals
3. Batch Data Parallel Processing with Dask
4. Distributed Systems and Dask
5. Advanced Dask: APIs and Building Blocks
6. Dask with Pandas
7. Dask with Scikit-learn
8. Dask and PyTorch
9. Dask with GPUs
10. Scaling Machine Learning Projects with Dask

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