Deep Learning with Jax (Paperback)
暫譯: 使用 Jax 的深度學習 (平裝本)

Sapunov, Grigory

  • 出版商: Manning
  • 出版日期: 2024-10-29
  • 售價: $2,270
  • 貴賓價: 9.5$2,157
  • 語言: 英文
  • 頁數: 408
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1633438880
  • ISBN-13: 9781633438880
  • 相關分類: DeepLearning
  • 立即出貨 (庫存 < 4)

買這商品的人也買了...

相關主題

商品描述

Accelerate deep learning and other number-intensive tasks with JAX, Google's awesome high-performance numerical computing library.

The JAX numerical computing library tackles the core performance challenges at the heart of deep learning and other scientific computing tasks. By combining Google's Accelerated Linear Algebra platform (XLA) with a hyper-optimized version of NumPy and a variety of other high-performance features, JAX delivers a huge performance boost in low-level computations and transformations.

In Deep Learning with JAX you will learn how to:

- Use JAX for numerical calculations
- Build differentiable models with JAX primitives
- Run distributed and parallelized computations with JAX
- Use high-level neural network libraries such as Flax
- Leverage libraries and modules from the JAX ecosystem

Deep Learning with JAX is a hands-on guide to using JAX for deep learning and other mathematically-intensive applications. Google Developer Expert Grigory Sapunov steadily builds your understanding of JAX's concepts. The engaging examples introduce the fundamental concepts on which JAX relies and then show you how to apply them to real-world tasks. You'll learn how to use JAX's ecosystem of high-level libraries and modules, and also how to combine TensorFlow and PyTorch with JAX for data loading and deployment.

Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications.

About the technology

Google's JAX offers a fresh vision for deep learning. This powerful library gives you fine control over low level processes like gradient calculations, delivering fast and efficient model training and inference, especially on large datasets. JAX has transformed how research scientists approach deep learning. Now boasting a robust ecosystem of tools and libraries, JAX makes evolutionary computations, federated learning, and other performance-sensitive tasks approachable for all types of applications.

About the book

Deep Learning with JAX teaches you to build effective neural networks with JAX. In this example-rich book, you'll discover how JAX's unique features help you tackle important deep learning performance challenges, like distributing computations across a cluster of TPUs. You'll put the library into action as you create an image classification tool, an image filter application, and other realistic projects. The nicely-annotated code listings demonstrate how JAX's functional programming mindset improves composability and parallelization.

What's inside

- Use JAX for numerical calculations
- Build differentiable models with JAX primitives
- Run distributed and parallelized computations with JAX
- Use high-level neural network libraries such as Flax

About the reader

For intermediate Python programmers who are familiar with deep learning.

About the author

Grigory Sapunov holds a Ph.D. in artificial intelligence and is a Google Developer Expert in Machine Learning.

The technical editor on this book was Nicholas McGreivy.

Table of Contents
Part 1
1 When and why to use JAX
2 Your first program in JAX
Part 2
3 Working with arrays
4 Calculating gradients
5 Compiling your code
6 Vectorizing your code
7 Parallelizing your computations
8 Using tensor sharding
9 Random numbers in JAX
10 Working with pytrees
Part 3
11 Higher-level neural network libraries
12 Other members of the JAX ecosystem
A Installing JAX
B Using Google Colab
C Using Google Cloud TPUs
D Experimental parallelization

商品描述(中文翻譯)

使用 JAX 加速深度學習及其他數值密集型任務,這是 Google 的高效能數值計算庫。

JAX 數值計算庫解決了深度學習及其他科學計算任務中的核心性能挑戰。通過將 Google 的加速線性代數平台 (XLA) 與超優化版本的 NumPy 及各種其他高效能功能相結合,JAX 在低層計算和轉換中提供了巨大的性能提升。

Deep Learning with JAX 中,您將學習如何:

- 使用 JAX 進行數值計算

- 使用 JAX 原語構建可微分模型

- 使用 JAX 執行分散式和並行計算

- 使用高階神經網絡庫,如 Flax

- 利用 JAX 生態系統中的庫和模組

Deep Learning with JAX 是一本實用指南,教您如何使用 JAX 進行深度學習及其他數學密集型應用。Google 開發者專家 Grigory Sapunov 穩步增進您對 JAX 概念的理解。引人入勝的範例介紹了 JAX 所依賴的基本概念,然後展示如何將其應用於實際任務。您將學會如何使用 JAX 的高階庫和模組生態系統,以及如何將 TensorFlow 和 PyTorch 與 JAX 結合進行數據加載和部署。

購買印刷版書籍可獲得 Manning Publications 提供的免費 PDF 和 ePub 格式電子書。

關於技術

Google 的 JAX 為深度學習提供了全新的視野。這個強大的庫讓您對低層過程(如梯度計算)進行精細控制,提供快速且高效的模型訓練和推斷,特別是在大型數據集上。JAX 改變了研究科學家對深度學習的看法。現在擁有強大的工具和庫生態系統,JAX 使得進化計算、聯邦學習及其他性能敏感型任務對各類應用變得可行。

關於本書

Deep Learning with JAX 教您如何使用 JAX 構建有效的神經網絡。在這本範例豐富的書中,您將發現 JAX 的獨特功能如何幫助您解決重要的深度學習性能挑戰,例如在 TPU 集群上分配計算。您將在創建圖像分類工具、圖像過濾應用及其他現實項目時實際運用這個庫。精美註解的代碼清單展示了 JAX 的函數式編程思維如何改善可組合性和並行化。

內容概覽

- 使用 JAX 進行數值計算

- 使用 JAX 原語構建可微分模型

- 使用 JAX 執行分散式和並行計算

- 使用高階神經網絡庫,如 Flax

讀者對象

適合熟悉深度學習的中階 Python 程式設計師。

關於作者

Grigory Sapunov 擁有人工智慧博士學位,並且是 Google 的機器學習開發者專家。

本書的技術編輯是 Nicholas McGreivy

目錄

第一部分

1 何時以及為何使用 JAX

2 您的第一個 JAX 程式

第二部分

3 操作陣列

4 計算梯度

5 編譯您的代碼

6 向量化您的代碼

7 並行化您的計算

8 使用張量分片

9 JAX 中的隨機數

10 操作 pytrees

第三部分

11 高階神經網絡庫

12 JAX 生態系統中的其他成員

A 安裝 JAX

B 使用 Google Colab

C 使用 Google Cloud TPU

D 實驗性並行化

作者簡介

Grigory Sapunov is a co-founder and CTO of Intento. He is a software engineer with more than twenty years of experience. Grigory holds a Ph.D. in artificial intelligence and is a Google Developer Expert in Machine Learning.

作者簡介(中文翻譯)

格里戈里·薩普諾夫是Intento的共同創辦人及首席技術官。他是一位擁有超過二十年經驗的軟體工程師。格里戈里擁有人工智慧的博士學位,並且是Google的機器學習開發者專家。