Deep Learning (Adaptive Computation and Machine Learning series)

Description

Deep Learning (Adaptive Computation and Machine Learning series)

Deep Learning is an introduction to the theory, methods and applications of deep learning, part of the Adaptive Computation and Machine Learning series. It brings together the mathematical foundations, the practical techniques and the current research directions of the field in one book.

The text opens with the mathematical and conceptual groundwork needed for deep learning: linear algebra, probability, information theory, numerical computation and classical machine learning. These sections prepare readers for both the applied and theoretical chapters that follow.

Core architectures and training methods

The book covers feedforward neural networks, regularization and optimization strategies, convolutional networks and sequence modeling, with practical material on recommendation systems and scientific computing.

Research-oriented chapters

Later chapters cover representation learning, autoencoders, probabilistic models, approximate inference, Monte Carlo methods and deep generative models, aimed at readers pursuing research or graduate level study.

Book details

• Title: Deep Learning
• ISBN-13: 9780262035613
• Genre: Artificial Intelligence & Machine Learning Textbook

Questions buyers ask

Who is this book for?

Students, researchers and software engineers who want a structured understanding of neural networks and modern machine learning systems.

Do I need a strong math background?

Yes. The book opens with linear algebra, probability, information theory, numerical computation and classical machine learning before moving into deep learning topics.

Does it cover research topics or just applications?

Both. Core architecture chapters cover applied work, and later chapters cover research topics such as representation learning and deep generative models.

What architectures does it cover?

Feedforward neural networks, convolutional networks and sequence modeling, along with regularization and optimization strategies.

Is it used in academic programs?

Yes. It is used in academic programs and professional settings as a reference for students, researchers and software engineers.