Название: Understanding Deep Learning
Автор: Simon J.D. Prince
Издательство: The MIT Press
Год: December 5, 2023
Страниц: 544
Язык: английский
Формат: pdf (true), epub (true)
Размер: 21.3 MB, 37.9 MB

An authoritative, accessible, and up-to-date treatment of Deep Learning that strikes a pragmatic middle ground between theory and practice.

Deep Learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics

Artificial Intelligence, or AI, is concerned with building systems that simulate intelligent behavior. It encompasses a wide range of approaches, including those based on logic, search, and probabilistic reasoning. Machine Learning is a subset of AI that learns to make decisions by fitting mathematical models to observed data. This area has seen explosive growth and is now (incorrectly) almost synonymous with the term AI.

A deep neural network is a type of Machine Learning model, and when it is fitted to data, this is referred to as Deep Learning. At the time of writing, deep networks are the most powerful and practical Machine Learning models and are often encountered in day-to-day life. It is commonplace to translate text from another language using a Natural Language Processing (NLP) algorithm, to search the internet for images of a particular object using a computer vision system, or to converse with a digital assistant via a speech recognition interface. All of these applications are powered by Deep Learning.

Up-to-date treatment of deep learning covers cutting-edge topics not found in existing texts, such as transformers and diffusion models
Short, focused chapters progress in complexity, easing students into difficult concepts
Pragmatic approach straddling theory and practice gives readers the level of detail required to implement naive versions of models
Streamlined presentation separates critical ideas from background context and extraneous detail
Minimal mathematical prerequisites, extensive illustrations, and practice problems make challenging material widely accessible
Programming exercises offered in accompanying Python Notebooks







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