Book Image

Advanced Python Programming - Second Edition

By : Quan Nguyen
Book Image

Advanced Python Programming - Second Edition

By: Quan Nguyen

Overview of this book

Python's powerful capabilities for implementing robust and efficient programs make it one of the most sought-after programming languages. In this book, you'll explore the tools that allow you to improve performance and take your Python programs to the next level. This book starts by examining the built-in as well as external libraries that streamline tasks in the development cycle, such as benchmarking, profiling, and optimizing. You'll then get to grips with using specialized tools such as dedicated libraries and compilers to increase your performance at number-crunching tasks, including training machine learning models. The book covers concurrency, a major solution to making programs more efficient and scalable, and various concurrent programming techniques such as multithreading, multiprocessing, and asynchronous programming. You'll also understand the common problems that cause undesirable behavior in concurrent programs. Finally, you'll work with a wide range of design patterns, including creational, structural, and behavioral patterns that enable you to tackle complex design and architecture challenges, making your programs more robust and maintainable. By the end of the book, you'll be exposed to a wide range of advanced functionalities in Python and be equipped with the practical knowledge needed to apply them to your use cases.
Table of Contents (32 chapters)
1
Section 1: Python-Native and Specialized Optimization
8
Section 2: Concurrency and Parallelism
18
Section 3: Design Patterns in Python

Chapter 6: Automatic Differentiation and Accelerated Linear Algebra for Machine Learning

With the recent explosion of data and data generating systems, machine learning has grown to be an exciting field, both in research and industry. However, implementing a machine learning model might prove to be a difficult endeavor. Specifically, common tasks in machine learning, such as deriving the loss function and its derivative, using gradient descent to find the optimal combination of model parameters, or using the kernel method for nonlinear data, demand clever implementations to make predictive models efficient.

In this chapter, we will discuss the JAX library, the premier high-performance machine learning tool in Python. We will explore some of its most powerful features, such as automatic differentiation, JIT compilation, and automatic vectorization. These features streamline the tasks that are central to machine learning mentioned previously, making training a predictive model as...