Demystifying Deep Learning: An Introduction to the Mathematics of Neural Networks

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.

發現如何通過學習構建真實的深度學習軟件庫和驗證軟件來訓練深度學習模型！ 深度學習和人工神經網絡（ANN）的研究是人工智能（AI）的一個重要子領域，可以在許多領域中找到，例如醫學、法律、金融服務和科學。正如機器人革命在20世紀70年代威脅到藍領工作一樣，現在AI革命為白領工作帶來了新的生產力時代。重要的任務已經開始由ANN接管，從疾病檢測和預防到閱讀和支持法律合同，再到理解實驗數據、模擬蛋白質折疊和颶風模擬。AI無處不在-在新聞中，在智庫中，佔據著世界各地的政策制定者-而ANN通常為AI提供支撐。

《深度學習解密》以一種非正式而簡潔的方式，是學習實施ANN算法所需步驟的有用工具，通過使用應用神經網絡訓練和驗證軟件的軟件庫。本書解釋了真實的ANN如何工作，並包含6個實際示例，展示了如何使用真實代碼構建ANN和它們在實施中所需的數據集，這些代碼在開源中提供，以確保實際使用。這本易於理解的書籍遵循了每天使用的ANN技術，並適應自然語言處理、圖像識別、問題解決和生成應用。本書是該領域的重要入門，為讀者進一步的研究提供了基礎。

《深度學習解密》的讀者還將找到：
- 強調分類的重要性
- 討論為什麼ANN庫（如Tensor Flow和Pytorch）是用C++而不是Python編寫的
- 每章結束時都有一個“項目”頁面，以促進學生實驗真實代碼
- 支持該書的軟件庫，網址為https://github.com/nom-de-guerre/RANT
- 易於理解的解釋，闡明生成AI（如生成對抗網絡（GAN））的工作原理
- 對於大型語言模型（LLM）如ChatGPT的基礎-transformer的動機和闡明

《深度學習解密》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，這也是一本有用的教材，可以對該主題有扎實的基礎。

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.

Douglas Santry博士是英國肯特大學計算機科學系的講師。Santry博士在劍橋大學獲得了博士學位。在目前的職位之前，他曾在蘋果電腦公司、NetApp和高盛等公司擔任重要職位。