Math and Architectures of Deep Learning

Math and Architectures of Deep Learning sets out the foundations of DL usefully and accessibly to working practitioners.

The mathematical paradigms that underlie deep learning typically start out as hard-to-read academic papers, often leaving engineers in the dark about how their models actually function. Math and Architectures of Deep Learning bridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementations in Python and PyTorch. You'll peer inside the "black box" to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology

It's important to understand how your deep learning models work, both so that you can maintain them efficiently and explain them to other stakeholders. Learning mathematical foundations and neural network architecture can be challenging, but the payoff is big. You'll be free from blind reliance on prepackaged DL models and able to build, customize, and re-architect for your specific needs. And when things go wrong, you'll be glad you can quickly identify and fix problems.

About the book

Math and Architectures of Deep Learning sets out the foundations of DL in a way that's both useful and accessible to working practitioners. Each chapter explores a new fundamental DL concept or architectural pattern, explaining the underpinning mathematics and demonstrating how they work in practice with well-annotated Python code. You'll start with a primer of basic algebra, calculus, and statistics, working your way up to state-of-the-art DL paradigms taken from the latest research. By the time you're done, you'll have a combined theoretical insight and practical skills to identify and implement DL architecture for almost any real-world challenge.

What's inside

• Math, theory, and programming principles side by side
• Linear algebra, vector calculus and multivariate statistics for deep learning
• The structure of neural networks
• Implementing deep learning architectures with Python and PyTorch
• Troubleshooting underperforming models
• Working code samples in downloadable Jupyter notebooks

About the reader

For Python programmers with algebra and calculus basics.

About the author

Krishnendu Chaudhury is a deep learning and computer vision expert with decade-long stints at both Google and Adobe Systems. He is presently CTO and co-founder of Drishti Technologies. He has a PhD in computer science from the University of Kentucky at Lexington.

《深度學習的數學與架構》將深度學習的基礎以實用且易懂的方式呈現給實際從業人員。深度學習的數學範式通常以難以閱讀的學術論文形式呈現，這常常讓工程師對模型的實際運作方式一無所知。《深度學習的數學與架構》填補了理論與實踐之間的鴻溝，並以Python和PyTorch實際實現的方式呈現深度學習的數學。你將深入了解代碼的運作方式，並學會理解最前沿的研究成果，將其轉化為實際應用。購買印刷版書籍還包括Manning Publications提供的PDF、Kindle和ePub格式的免費電子書。

關於技術方面，了解深度學習模型的運作方式非常重要，這樣你才能高效地維護它們並向其他利益相關者解釋。學習數學基礎和神經網絡架構可能具有挑戰性，但回報很大。你將不再盲目依賴預打包的深度學習模型，而能夠根據自己的需求構建、自定義和重新架構。當出現問題時，你會很高興能夠快速識別和解決問題。

《深度學習的數學與架構》以對實際從業人員有用且易懂的方式介紹了深度學習的基礎。每一章探討一個新的基礎深度學習概念或架構模式，解釋其基礎數學原理，並通過註釋完善的Python代碼展示其實際運作方式。你將從基礎的代數、微積分和統計學開始，逐步深入到最新研究中採用的深度學習範式。完成後，你將具備理論洞察力和實踐技能，能夠為幾乎任何現實世界的挑戰識別和實現深度學習架構。

內容包括：
- 數學、理論和編程原則並列
- 線性代數、向量微積分和多變量統計學在深度學習中的應用
- 神經網絡的結構
- 使用Python和PyTorch實現深度學習架構
- 解決性能不佳的模型問題
- 可下載的Jupyter筆記本中的工作代碼示例

適合具備代數和微積分基礎的Python程序員閱讀。

關於作者：
Krishnendu Chaudhury是一位深度學習和計算機視覺專家，曾在Google和Adobe Systems工作了十年。他目前是Drishti Technologies的首席技術官和聯合創始人。他在肯塔基大學獲得計算機科學博士學位。

Krishnendu Chaudhury is a deep learning and computer vision expert with decade-long stints at both Google and Adobe Systems. He is presently CTO and co-founder of Drishti Technologies. He has a PhD in computer science from the University of Kentucky at Lexington.

Krishnendu Chaudhury是一位深度學習和計算機視覺專家，曾在Google和Adobe Systems工作了十年。他目前是Drishti Technologies的首席技術官和共同創辦人。他在肯塔基大學列克星敦分校獲得了計算機科學博士學位。