PyTorch Deep Learning Hands-On: Apply modern AI techniques with CNNs, RNNs, GANs, reinforcement learning, and more (PyTorch 深度學習實戰:應用現代 AI 技術於 CNN、RNN、GAN、強化學習等)
Sherin Thomas
- 出版商: Packt Publishing
- 出版日期: 2019-04-26
- 定價: $1,600
- 售價: 8.0 折 $1,280
- 語言: 英文
- 頁數: 304
- 裝訂: Paperback
- ISBN: 1788834135
- ISBN-13: 9781788834131
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相關分類:
DeepLearning
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相關翻譯:
PyTorch 深度學習實戰 (簡中版)
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相關主題
商品描述
Developing image analysis apps, GAN-based networks, reinforcement learning algorithms and text engineering routines with Deep Learning PyTorch applications
Key Features
- The first book-length introduction to PyTorch
- Covers the whole range of possible applications that can be written on PyTorch
- Focuses on the APIs, and treats algorithms as secondary
Book Description
Deep Learning is probably the fastest-growing, but also the most complex area of applied computing today. There are two major frameworks dominating the Deep Learning API landscape - Google's TensorFlow, and Facebook's PyTorch. Deriving from the open source Torch framework written in Lua, it was under the leadership of AI guru Yann LeCun that Pytorch developed into a major alternative.
PyTorch uses autodifferentiation to make it possible for developers to introduce new behaviors into their neural networks, without having to restart their networks. This is possibly the most important innovation for major machine and deep learning frameworks implemented in Pytorch. Also, PyTorch threads can run on CPUs as well as GPUs, providing major efficiency gains in the process.
This book shows us how to make the simplicity and power of Pytorch work for a Python developer. The first application we learn about is how how to process images using CNNs, but new algorithms like GANs and and natural language processing algorithms are introduced as well. The book ends with a chapter on reinforcement learning and how put PyTorch application into production
What you will learn
- Processing, improving and recognizing image features
- Finding, interpreting and deriving insights from unstructured textual data
- Learning several varieties of General Adversarial Networks (GANs)
- Apply PyTorch implementations of reinforcement learning algorithms
- Put PyTorch projects through a production cycle
Who This Book Is For
Fluency in Python is assumed. Basic deep learning approaches should be familiar to the reader. This book is meant to be an introduction to PyTorch, and tries to show the breadth of applications PyTorch can be put to.
商品描述(中文翻譯)
開發基於深度學習PyTorch應用的圖像分析應用程式、基於GAN的網絡、強化學習算法和文本工程例程。
主要特點:
- 第一本介紹PyTorch的書籍
- 涵蓋了可以在PyTorch上編寫的所有應用範圍
- 將API視為重點,將算法視為次要
書籍描述:
深度學習可能是當今應用計算中增長最快、也最複雜的領域。在深度學習API領域,有兩個主要的框架佔據主導地位- Google的TensorFlow和Facebook的PyTorch。PyTorch源於用Lua編寫的開源Torch框架,在AI專家Yann LeCun的領導下,PyTorch成為了一個重要的替代品。
PyTorch使用自動微分使開發人員能夠在神經網絡中引入新的行為,而無需重新啟動網絡。這對於在PyTorch中實現的主要機器和深度學習框架可能是最重要的創新。此外,PyTorch線程可以在CPU和GPU上運行,從而在過程中提供主要的效率提升。
本書向我們展示了如何讓PyTorch的簡單性和強大性適用於Python開發人員。我們首先學習的應用是如何使用CNN處理圖像,但也介紹了新的算法,如GAN和自然語言處理算法。書的最後一章介紹了強化學習以及如何將PyTorch應用投入生產。
你將學到什麼:
- 處理、改進和識別圖像特徵
- 從非結構化文本數據中尋找、解釋和獲取洞察
- 學習多種類型的生成對抗網絡(GAN)
- 應用PyTorch實現的強化學習算法
- 將PyTorch項目進行生產週期
適合閱讀者:
假設讀者熟悉Python並了解基本的深度學習方法。本書旨在介紹PyTorch,並試圖展示PyTorch可以應用的廣度。