Deep Learning: Foundations and Concepts

Deep Learning: Foundations and Concepts by Christopher M. Bishop and Hugh Bishop is a comprehensive textbook introducing the fundamental concepts, mathematical foundations, architectures, and techniques used in modern deep learning.

Published by Springer, the book is designed for students, researchers, software engineers, and readers developing an understanding of deep learning and machine learning. It combines mathematical explanations with diagrams, formulas, and pseudocode to explain important concepts in a structured progression.

The book covers probability, neural networks, regression and classification, deep neural networks, gradient descent, backpropagation, regularization, convolutional networks, transformers, graph neural networks, latent-variable models, generative adversarial networks, normalizing flows, autoencoders, and diffusion models.

Key Topics

• Deep Learning
• Machine Learning
• Artificial Intelligence
• Neural Networks
• Deep Neural Networks
• Probability
• Regression
• Classification
• Gradient Descent
• Backpropagation
• Regularization
• Convolutional Neural Networks
• Transformers
• Graph Neural Networks
• Generative AI
• Generative Adversarial Networks
• Autoencoders
• Diffusion Models
• Latent Variables
• Machine Learning Applications