This book offers a comprehensive introduction to the central ideas that underpin deep learning. It is intended both for newcomers to machine learning and for those already experienced in the field. Covering key concepts relating to contemporary architectures and techniques, this essential book equips readers with a robust foundation for potential future specialization. The field of deep learning is undergoing rapid evolution, and therefore this book focusses on ideas that are likely to endure the test of time.The book is organized into numerous bite-sized chapters, each exploring a distinct topic, and the narrative follows a linear progression, with each chapter building upon content from its predecessors. This structure is well-suited to teaching a two-semester undergraduate or postgraduate machine learning course, while remaining equally relevant to those engaged in active research or in self-study. A full understanding of machine learning requires some mathematical background and so the book includes a self-contained introduction to probability theory. However, the focus of the book is on conveying a clear understanding of ideas, with emphasis on the real-world practical value of techniques rather than on abstract theory. Complex concepts are therefore presented from multiple complementary perspectives including textual descriptions, diagrams, mathematical formulae, and pseudo-code.Chris Bishop is a Technical Fellow at Microsoft and is 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 completed his MPhil in Machine Learning and Machine Intelligence at Cambridge University.“Chris Bishop wrote a terrific textbook on neural networks in 1995 and has a deep knowledge of the field and its core ideas. His many years of experience in explaining neural networks have made him extremely skillful at presenting complicated ideas in the simplest possible way and it is a delight to see these skills applied to the revolutionary new developments in the field.” -- Geoffrey Hinton’With the recent explosion of deep learning and AI as a research topic, and the quickly growing importance of AI applications, a modern textbook on the topic was badly needed. The ’New Bishop’ masterfully fills the gap, covering algorithms for supervised and unsupervised learning, modern deep learning architecture families, as well as how to apply all of this to various application areas.’ – Yann LeCun “This excellent and very educational book will bring the reader up to date with the main concepts and advances in deep learning with a solid anchoring in probability. These concepts are powering current industrial AI systems and are likely to form the basis of further advances towards artificial general intelligence.” -- Yoshua Bengio

• 关于产品：
• ● 正版保障：本网站隶属于中国国际图书贸易集团公司，确保所有图书都是100%正版。
• ● 环保纸张：进口图书大多使用的都是环保轻型张，颜色偏黄，重量比较轻。
• ● 毛边版：即书翻页的地方，故意做成了参差不齐的样子，一般为精装版，更具收藏价值。
• 
  关于退换货：
• 由于预订产品的特殊性，采购订单正式发订后，买方不得无故取消全部或部分产品的订购。
• 由于进口图书的特殊性，发生以下情况的，请直接拒收货物，由快递返回：
• ● 外包装破损/发错货/少发货/图书外观破损/图书配件不全（例如：光盘等）
  并请在工作日通过电话400-008-1110联系我们。
• 签收后，如发生以下情况，请在签收后的5个工作日内联系客服办理退换货：
• ● 缺页/错页/错印/脱线
• 
  关于发货时间：
• 一般情况下：
• ●【现货】 下单后48小时内由北京（库房）发出快递。
• ●【预订】【预售】下单后国外发货，到货时间预计5-8周左右，店铺默认中通快递，如需顺丰快递邮费到付。
• ● 需要开具发票的客户，发货时间可能在上述基础上再延后1-2个工作日（紧急发票需求，请联系010-68433105/3213）；
• ● 如遇其他特殊原因，对发货时间有影响的，我们会第一时间在网站公告，敬请留意。
• 
  关于到货时间：
• 由于进口图书入境入库后，都是委托第三方快递发货，所以我们只能保证在规定时间内发出，但无法为您保证确切的到货时间。
• ● 主要城市一般2-4天
• ● 偏远地区一般4-7天
• 
  关于接听咨询电话的时间：
• 010-68433105/3213正常接听咨询电话的时间为：周一至周五上午8：30～下午5：00，周六、日及法定节假日休息，将无法接听来电，敬请谅解。
• 其它时间您也可以通过邮件联系我们：customer@readgo.cn,工作日会优先处理。
• 
  关于快递：
• ● 已付款订单：主要由中通、宅急送负责派送，订单进度查询请拨打010-68433105/3213。