Demystifying Deep Learning: An Introduction to the Mathematics of Neural Networks
暫譯: 揭開深度學習的神秘面紗:神經網絡數學入門

Santry, Douglas J.

  • 出版商: Wiley
  • 出版日期: 2023-12-12
  • 售價: $4,410
  • 貴賓價: 9.5$4,190
  • 語言: 英文
  • 頁數: 256
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 1394205600
  • ISBN-13: 9781394205608
  • 相關分類: DeepLearning
  • 海外代購書籍(需單獨結帳)

商品描述

Discover how to train Deep Learning models by learning how to build real Deep Learning software libraries and verification software!

The study of Deep Learning and Artificial Neural Networks (ANN) is a significant subfield of artificial intelligence (AI) that can be found within numerous fields: medicine, law, financial service, and science, for example. Just as the robot revolution threatened blue-collar jobs in the 1970s, so now the AI revolution promises a new era of productivity for white collar jobs. Important tasks have begun being taken over by ANNs, from disease detection and prevention to reading and supporting legal contracts, to understanding experimental data, model protein folding, and hurricane modeling. AI is everywhere--on the news, in think tanks, and occupies government policy makers all over the world --and ANNs often provide the backbone for AI.

Relying on an informal and succinct approach, Demystifying Deep Learning is a useful tool to learn the necessary steps to implement ANN algorithms by using both a software library applying neural network training and verification software. The volume offers explanations of how real ANNs work, and includes 6 practical examples that demonstrate in real code how to build ANNS and the datasets they need in their implementation, available in open-source to ensure practical usage. This approachable book follows ANN techniques that are used every day as they adapt to natural language processing, image recognition, problem solving, and generative applications. This volume is an important introduction to the field equipping the reader for more advanced study.

Demystifying Deep Learning readers will also find:

  • A volume that emphasizes the importance of classification
  • Discussion of why ANN libraries (such as Tensor Flow and Pytorch) are written in C++ rather than Python
  • Each chapter concludes with a "Projects" page to promote students experimenting with real code
  • A supporting library of software to accompany the book at https: //github.com/nom-de-guerre/RANT
  • Approachable explanation of how generative AI, such as generative adversarial networks (GAN) really work.
  • An accessible motivation and elucidation of how transformers, the basis of large language models (LLM) such as ChatGPT, work.

Demystifying Deep Learning is ideal for engineers and professionals that need to learn and understand ANNs in their work. It is also a helpful text for advanced undergraduates to get a solid grounding on the topic.

商品描述(中文翻譯)

探索如何透過建立真正的深度學習軟體庫和驗證軟體來訓練深度學習模型!

深度學習和人工神經網路(ANN)的研究是人工智慧(AI)的一個重要子領域,廣泛應用於醫學、法律、金融服務和科學等多個領域。正如1970年代的機器人革命威脅到藍領工作一樣,現在的AI革命則承諾為白領工作帶來新的生產力時代。重要任務已經開始由ANN接管,從疾病檢測和預防到閱讀和支持法律合約,再到理解實驗數據、模型蛋白質摺疊和颶風建模。AI無處不在——在新聞中、智庫中,並且佔據了全球各地的政府政策制定者的注意力——而ANN通常為AI提供了基礎。

《揭開深度學習的神秘面紗》採用非正式且簡潔的方式,是學習實施ANN演算法所需步驟的有用工具,使用一個應用神經網路訓練的軟體庫和驗證軟體。本書提供了真實ANN運作的解釋,並包括6個實用範例,展示如何在實際代碼中構建ANN及其實施所需的數據集,這些數據集以開源形式提供以確保實用性。這本易於接近的書籍遵循日常使用的ANN技術,適應自然語言處理、圖像識別、問題解決和生成應用。本書是該領域的重要入門,為讀者提供進一步深入學習的基礎。

《揭開深度學習的神秘面紗》的讀者還會發現:


  • 強調分類重要性的卷冊

  • 討論為何ANN庫(如Tensor Flow和Pytorch)是用C++而非Python編寫的

  • 每章結尾都有一個「專案」頁面,促進學生實驗真實代碼

  • 隨書附帶的支援軟體庫,網址為 https://github.com/nom-de-guerre/RANT

  • 對生成式AI(如生成對抗網路GAN)如何真正運作的易於理解的解釋

  • 對大型語言模型(LLM)如ChatGPT的基礎——變壓器的動機和闡述,易於接近

《揭開深度學習的神秘面紗》非常適合需要在工作中學習和理解ANN的工程師和專業人士。對於高年級本科生來說,這也是一本幫助他們在該主題上打下堅實基礎的有用教材。

作者簡介

Douglas Santry, PhD, is a lecturer in Computer Science at the University of Kent, UK. Dr. Santry obtained his PhD from the University of Cambridge. Prior to his current position, he worked extenstively as an important figure in industry with Apple Computer Corp, NetApp and Goldman Sachs.

作者簡介(中文翻譯)

道格拉斯·桑特里(Douglas Santry),博士,是英國肯特大學計算機科學的講師。桑特里博士在劍橋大學獲得博士學位。在擔任目前職位之前,他曾在業界擔任重要角色,並在蘋果電腦公司(Apple Computer Corp)、網絡應用公司(NetApp)和高盛(Goldman Sachs)工作。