{"product_id":"introduction-to-deep-learning-sandro-skansi-9783032254597","title":"Introduction to Deep Learning: Neural Networks, Large Language Models and Agentic AI","description":"\u003cp\u003eThis 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. \u003c\/p\u003e \u003cp\u003eThe \u003cstrong\u003esecond edition\u003c\/strong\u003e 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.\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eTopics and features: \u003c\/strong\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eIntroduces fundamentals of machine learning and mathematical and computational prerequisites for deep learning\u003c\/li\u003e \u003cli\u003eDiscusses feed-forward neural networks, convolutional networks, and recurrent architectures, and explores the modifications applicable to any neural network\u003c\/li\u003e \u003cli\u003eCovers the transformer architecture from first principles, including self-attention, multi-head attention, positional encoding, and a minimal annotated implementation\u003c\/li\u003e \u003cli\u003eReviews open research problems, from hallucinations and quadratic scaling to alignment faking and the interpretability of model internals\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThis proven, \u003cem\u003efully\u003c\/em\u003e \u003cem\u003erevised\u003c\/em\u003e textbook is written for graduate and advanced undergraduate students of computer science, cognitive science, and mathematics. It should prove equally valuable for readers in linguistics, logic, philosophy, and psychology.\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eSandro Skansi \u003c\/strong\u003eis an Associate Professor at the University of Zagreb, Croatia, where he teaches logic, political philosophy, artificial intelligence, and cognitive science. \u003cstrong\u003eKristina Sekrst\u003c\/strong\u003e is a research associate at the University of Zagreb and a principal engineer at Preamble AI.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Sandro Skansi,Kristina Sekrst\u003cbr\u003e\u003cb\u003eISBN-10:\u003c\/b\u003e 3032254590\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9783032254597\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Springer\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 12\/18\/2026\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 104\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback","brand":"Sandro Skansi","offers":[{"title":"Paperback","offer_id":49001576923391,"sku":"9783032254597","price":54.99,"currency_code":"USD","in_stock":false}],"url":"https:\/\/www.whiterainbookhouse.com\/products\/introduction-to-deep-learning-sandro-skansi-9783032254597","provider":"WR Book House","version":"1.0","type":"link"}