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

Über die Autorin bzw. den Autor

Douglas J. 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.

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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 services, 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
• An 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.