Deep Learning: Foundations and Concepts PDF CLEAN
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This book provides a thorough introduction to the fundamental concepts of deep learning, catering to both newcomers and those with experience in machine learning. It covers essential ideas related to modern architectures and techniques, offering readers a strong foundation for future specialization. Given the rapid evolution of deep learning, the focus is on concepts that are likely to remain relevant over time.
Organized into concise chapters, each addressing a specific topic, the book follows a linear progression where each chapter builds on the previous ones. This structure is ideal for teaching a two-semester undergraduate or postgraduate course in machine learning, while also being beneficial for active researchers or self-learners.
A solid understanding of machine learning necessitates some mathematical knowledge, so the book includes a self-contained introduction to probability theory. However, the primary goal is to communicate clear ideas, emphasizing the practical applications of techniques over abstract theory. Complex concepts are presented from various perspectives, including textual explanations, diagrams, mathematical formulas, and pseudo-code.
Chris Bishop is a Technical Fellow at Microsoft and the Director of Microsoft Research AI4Science. He is a Fellow of Darwin College, Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society.
Hugh Bishop is an Applied Scientist at Wayve, a deep learning autonomous driving company in London, where he designs and trains deep neural networks. He holds an MPhil in Machine Learning and Machine Intelligence from Cambridge University.
“Chris Bishop wrote a fantastic textbook on neural networks in 1995 and possesses deep knowledge of the field. His extensive experience in explaining neural networks has honed his ability to present complex ideas simply, making it a pleasure to see these skills applied to the groundbreaking developments in the field.” — Geoffrey Hinton
"With the recent surge in deep learning and AI as research topics, and the growing importance of AI applications, a modern textbook was urgently needed. The 'New Bishop' expertly fills this gap, covering algorithms for supervised and unsupervised learning, contemporary deep learning architectures, and their applications." — Yann LeCun
“This excellent and highly educational book brings readers up to speed with the main concepts and advancements in deep learning, grounded in probability. These concepts are driving current industrial AI systems and are likely to underpin future progress toward artificial general intelligence.” — Yoshua Bengio
Organized into concise chapters, each addressing a specific topic, the book follows a linear progression where each chapter builds on the previous ones. This structure is ideal for teaching a two-semester undergraduate or postgraduate course in machine learning, while also being beneficial for active researchers or self-learners.
A solid understanding of machine learning necessitates some mathematical knowledge, so the book includes a self-contained introduction to probability theory. However, the primary goal is to communicate clear ideas, emphasizing the practical applications of techniques over abstract theory. Complex concepts are presented from various perspectives, including textual explanations, diagrams, mathematical formulas, and pseudo-code.
Chris Bishop is a Technical Fellow at Microsoft and the Director of Microsoft Research AI4Science. He is a Fellow of Darwin College, Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society.
Hugh Bishop is an Applied Scientist at Wayve, a deep learning autonomous driving company in London, where he designs and trains deep neural networks. He holds an MPhil in Machine Learning and Machine Intelligence from Cambridge University.
“Chris Bishop wrote a fantastic textbook on neural networks in 1995 and possesses deep knowledge of the field. His extensive experience in explaining neural networks has honed his ability to present complex ideas simply, making it a pleasure to see these skills applied to the groundbreaking developments in the field.” — Geoffrey Hinton
"With the recent surge in deep learning and AI as research topics, and the growing importance of AI applications, a modern textbook was urgently needed. The 'New Bishop' expertly fills this gap, covering algorithms for supervised and unsupervised learning, contemporary deep learning architectures, and their applications." — Yann LeCun
“This excellent and highly educational book brings readers up to speed with the main concepts and advancements in deep learning, grounded in probability. These concepts are driving current industrial AI systems and are likely to underpin future progress toward artificial general intelligence.” — Yoshua Bengio
Listed on 16 August, 2024