Introduction to Deep Learning: Neural Networks, Large Language Models and Agentic AI (Undergraduate Topics in Computer Science)

This textbook introduces deep learning in a style that is accessible, rigorous, and grounded in working code.It walks through the most widely used algorithms and architectures step by step, with mathematical derivations kept intuitive and Python examples woven through every chapter.The second edition keeps everything from the first, including convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks, and autoencoders.It then covers the systems that have reshaped the field since: generative adversarial networks, the transformer architecture and its attention mechanism, the full training pipeline behind modern large language models (LLMs), prompt engineering with real-life guardrail scenarios, parameter-efficient fine-tuning with LoRA, retrieval-augmented generation with vector databases, knowledge graphs, and agentic AI systems illustrated through an industrial case study. Topics and features:Introduces fundamentals of machine learning and mathematical