Understanding Deep Learning (dhl)

An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice.

Deep learning is a fast-moving field with sweeping relevance in today's increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics.

• Up-to-date treatment of deep learning covers cutting-edge topics not found in existing texts, such as transformers and diffusion models
• Short, focused chapters progress in complexity, easing students into difficult concepts
• Pragmatic approach straddling theory and practice gives readers the level of detail required to implement naive versions of models
• Streamlined presentation separates critical ideas from background context and extraneous detail
• Minimal mathematical prerequisites, extensive illustrations, and practice problems make challenging material widely accessible
• Programming exercises offered in accompanying Python Notebooks

《深度學習的理論與實踐》是一本權威、易於理解且最新的深度學習著作，它在理論和實踐之間取得了實用的平衡。深度學習是一個快速發展的領域，在當今日益數位化的世界中具有廣泛的重要性。《深度學習的理論與實踐》提供了一個權威、易於理解且最新的深度學習內容，涵蓋了所有關鍵主題以及最新進展和尖端概念。許多深度學習的教材充斥著技術細節，使基礎知識變得模糊不清，但是Simon Prince無情地篩選出最重要的思想，以直觀且易於理解的形式提供高密度的關鍵信息。從機器學習的基礎知識到高級模型，每個概念都以通俗的語言呈現，然後以精確的數學形式進行詳細解釋並以視覺方式呈現。結果是一本清晰、自成體系的教材，適合具有應用數學基礎的任何人閱讀。

- 最新的深度學習內容涵蓋了現有教材中找不到的尖端主題，如轉換器和擴散模型。
- 短小而集中的章節按照複雜性進行進展，使學生更容易理解困難的概念。
- 實用的方法融合了理論和實踐，為讀者提供了實施模型的細節。
- 精簡的呈現方式將關鍵思想與背景內容和無關的細節區分開來。
- 最低限度的數學先備知識、豐富的插圖和練習問題使難以理解的材料變得廣泛可及。
- 附帶的Python筆記本提供了編程練習。

Simon J. D. Prince is Honorary Professor of Computer Science at the University of Bath and author of Computer Vision: Models, Learning and Inference. A research scientist specializing in artificial intelligence and deep learning, he has led teams of research scientists in academia and industry at Anthropics Technologies Ltd, Borealis AI, and elsewhere.

Simon J. D. Prince是巴斯大學的榮譽計算機科學教授，也是《計算機視覺：模型、學習和推理》一書的作者。作為一位專注於人工智能和深度學習的研究科學家，他曾在Anthropics Technologies Ltd、Borealis AI等學術界和工業界的研究科學家團隊中擔任領導職務。