Elements of Deep Learning

by Benyamin Ghojogh

Description

This textbook offers a comprehensive introduction to deep learning and neural networks, integrating core foundations with the latest advances. It begins with essential machine learning concepts and classic neural network architectures before progressing through convolutional models, backpropagation, regularization, generalization theory, PAC learning, and Boltzmann machines. Advanced chapters cover sequence models — including recurrent networks, LSTMs, attention, Transformers, state-space models, and large language models — alongside deep generative approaches such as VAEs, GANs, and diffusion models. Emerging topics include graph neural networks, self-supervised learning, metric learning, reinforcement learning, meta-learning, model compression, and knowledge distillation. Balancing mathematical rigor with hands-on practice, Elements of Deep Learning emphasizes both theoretical depth and real-world application. Different theories are introduced with PyTorch-based code examples, helping readers to translate theory into implementation. Organized into five sections—fundamentals, sequence models, generative models, emerging topics, and practice—the text provides a unified roadmap for mastering modern deep learning. Designed for advanced undergraduates, graduate students, instructors, and professionals in engineering, computer science, mathematics, and related fields, this book serves both as a primary course text and a reliable reference. With minimal prerequisites in linear algebra and calculus, it offers accessible explanations while equipping readers with practical tools for applications in vision, language, signal processing, healthcare, and beyond.

ISBN-13: 3032107385

Publisher: Springer Nature

Language: English

Publish Year: 2026

Benyamin Ghojogh's "Elements of Deep Learning" provides a rigorous and comprehensive introduction to the foundational principles and mathematical underpinnings of deep learning. The book systematically navigates through the core concepts, starting with the basic building blocks of artificial neural networks, such as perceptrons and activation functions, and gradually building towards more complex architectures. It emphasizes the importance of linear algebra, calculus, and probability theory as essential prerequisites, ensuring that readers develop a robust theoretical framework for understanding the mechanisms behind deep learning models.

The text meticulously details various types of neural network architectures, including Multi-Layer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). For each architecture, Ghojogh elaborates on their specific structures, operational principles, and common applications, such as image recognition, natural language processing, and time-series analysis. A significant portion is dedicated to the learning process, explaining loss functions, optimization algorithms like gradient descent and its variants, and the crucial backpropagation algorithm with detailed mathematical derivations, ensuring a deep grasp of how these models learn from data.

Furthermore, "Elements of Deep Learning" addresses practical considerations vital for effective model development, including regularization techniques to combat overfitting, initialization strategies, and hyperparameter tuning. It aims to equip readers with both theoretical knowledge and practical insights into designing, training, and evaluating deep learning systems. By striking a balance between mathematical rigor and intuitive explanations, the book serves as an invaluable resource for students, researchers, and practitioners looking to establish a solid foundation in the rapidly evolving field of deep learning.

Looking to add this masterpiece to your bookshelf? Compare prices and buy directly using our trusted partners:

As an Associate, BOOKLOVE earns from qualifying purchases. Read our full Affiliate Disclosure.