Deep Learning: 用Python進行深度學習的基礎理論實作 (1版)

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書名：DEEP LEARNING｜用PYTHON進行深度學習的基礎理論實作 出版社：歐萊禮 出版年月：201708 條碼：9789864764846 內容簡介 不走捷徑，幫助您真正搞懂「深度學習」的真義 這是一本與「深度學習」有關的書籍。從入門開始說明，一步一步帶領你瞭解深度學習必須具備的知識。本書可以幫助您了解：深度學習究竟是什麼？有何特色？根據何種原理來運作？ 從零開始，由實做中學習 本書的目標是，盡量避免使用不瞭解內容的「黑盒子」，以基礎的知識為起點，以容易上手的Python撰寫程式，從動手實作的過程中，一步步深入瞭解深度學習。若以車用書籍來比喻這本書的話，這本書並不屬於汽車駕訓教材，而是希望能夠幫助您瞭解車子的原理，而非教您開車的方法。為了瞭解汽車的結構，必須試著打開車子的引擎蓋，將每個零件都拿起來觀察、操作看看。然後盡量用簡單的形狀，篩選出車子的核心部分，就像組合迷你模型般，製作出這台車子。本書的目標，就是透過製作車子的過程，讓你感受到自己實際可以製作出車子，進而熟悉與車子的相關技術。 本書特色： ．利用最少的外部函式庫，使用Python，從零開始實際執行深度學習的程式。 ．說明Python 的用法，讓Python 的初學者也能理解。 ．實際執行Python 的原始碼，同時提供讀者手邊可以進行實驗的學習環境。 ．從簡單的機器學習問題開始，到最後執行精密辨識影像的系統。 ．以淺顯易懂的方式說明深度學習與神經網路理論。 ．針對看似複雜的技術，如誤差反向傳播與卷積運算等，利用實際操作方式說明，幫助理解。 ．介紹在執行深度學習時，有幫助且實用的技巧，包括決定學習率的方法、權重的預設值等。 ．說明Batch Normalization、Dropout、Adam 等最近的趨勢與操作。 ．為什麼深度學習很優秀，為什麼加深層數，就能提高辨識準確度，為什麼隱藏層很重要，仔細說明這些「為什麼」。 ．介紹自動運作、產生影像、強化學習等深度學習的應用範例。 作者介紹 作者簡介 斎藤康毅 1984年生於長崎縣對馬，畢業於東京工業大學工學院，東京大學研究所學際情報學府學士課程修畢。現在於企業內從事與電腦視覺、機器學習有關的研究開發工作。1984年生於長崎縣對馬，畢業於東京工業大學工學院，東京大學研究所學際情報學府學士課程修畢。現在於企業內從事與電腦視覺、機器學習有關的研究開發工作。 目錄 第一章 Python入門 第二章 感知器 第三章 神經網路 第四章 神經網路的學習 第五章 誤差反向傳播法 第六章 與學習有關的技巧 第七章 卷積神經網路 第八章 深度學習 附錄A Softmax-with-Loss層的計算圖 參考文獻

Introduction to Computing and Programming in Python (4版)

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【原文書】 書名：Introduction to Computing and Programming in Python 4/e Global Edition 作者：Guzdial 出版社：PEARSON 出版日期：2016/06/00 ISBN：9781292109862 Table of Contents 1 Introduction to Computer Science and Media Computation 2 Introduction to Programming 3 Creating and Modifying Text 4 Modifying Pictures Using Loops 5 Picture Techniques with Selection 6 Modifying Pixels by Position 7 Modifying Sounds Using Loops 8 Modifying Samples in a Range 9 Making Sounds by Combining Pieces 10 Building Bigger Programs 11 Manipulating Text with Methods and Files 12 Advanced Text Techniques: Web and Information 13 Making Text for the Web 14 Creating and Modifying Movie6 15 Speed 16 Functional Programming 17 Object Oriented Programming

INTRODUCTION TO PYTHON PROGRAMMING (Taiwan Edition / 含Access Code, 刮除不受退) (1版)

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【簡介】 Hallmark Features Bring programming to life with dynamic content designed for the way today's students read, think, and learn * Interactives and videos interwoven throughout the content enable students to practice essential coding skills in context. Thanks to this media-rich presentation of content, students come to class better prepared and ready to develop problem-solving skills through programming fundamentals. - VideoNotes are narrated step-by-step video tutorials that show how to solve problems completely, from design through coding (available on the companion website). - Listings step students through the code line-by-line, showing and explaining what is happening in the program. - Algorithm Animations show how an algorithm work visually and interactively (available on the companion website). - Notes, Tips, and Key Points offer insight and valuable advice on important aspects of programming. A fundamentals-first approach and clear framework foster understanding of key concepts * Teaches programming in a problem-driven way that focuses on problem solving rather than syntax using a fundamentals-first and problem-driven strategy, moving students at a steady pace through all the necessary and important basic concepts and techniques on selections, loops, functions, before writing custom classes. * Basic programming concepts are explained by examples from many different areas—math, science, business, finance, gaming, animation, and multimedia—and are supported by a variety of interactive learning activities to keep students engaged. * Step-by-step presentation guides students in learning all essential subjects in Python incrementally from fundamental programming techniques to object-oriented programming, from simple functions to STL. -- -- This new edition is completely revised in every detail to enhance clarity, presentation, content, examples, and exercises. The major improvements are as follows: * Section 1.2 is updated to include cloud storage and touchscreens. * Section 3.14 introduces the new Python 3.10 match-case statements to simplify coding for multiple cases. * F-strings are covered in Chapter 4 to provide a concise syntax to format strings for output. * Statistics functions are covered in Chapter 7 to enable students to write simple code for common statistics tasks. * Sections 14.4, 14.6 are split into multiple subsections to improve the presentation of the contents. * This edition provides many new examples and exercises to motivate and stimulate student interest in programming. * Additional exercises not printed in the book are available to adopting instructors. 【目錄】 1. Introduction to Computers, Programs, and Python 2. Elementary Programming 3. Selections 4. Mathematical Functions, Strings, and Objects 5. Loops 6. Functions 7. Lists 8. Multidimensional Lists 9. Objects and Classes 10. Basic GUI Programming Using Tkinter 11. Advanced GUI Programming Using Tkinter 12. Inheritance and Polymorphism 13. Files and Exception Handling 14. Tuples, Sets, and Dictionaries 15. Recursion Appendix A. Python Keywords Appendix B. The ASCII Character Set Appendix C. Number Systems Appendix D. Command Line Arguments Appendix E. Regular Expressions Appendix F. Bitwise Operations Appendix G. The Big-O, Big-Omega, and Big-Theta Notations Appendix H. Operator Precedence Chart Symbol Index Supplemental Material Glossary

An Introduction to Python Programming for Scientists and Engineers 2023

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Python is one of the most popular programming languages, widely used for data analysis and modelling, and is fast becoming the leading choice for scientists and engineers. Unlike other textbooks introducing Python, typically organised by language syntax, this book uses many examples from across Biology, Chemistry, Physics, Earth science, and Engineering to teach and motivate students in science and engineering. The text is organised by the tasks and workflows students undertake day-to-day, helping them see the connections between programming tools and their disciplines. The pace of study is carefully developed for complete beginners, and a spiral pedagogy is used so concepts are introduced across multiple chapters, allowing readers to engage with topics more than once. ＂Try This!＂ exercises and online Jupyter notebooks encourage students to test their new knowledge, and further develop their programming skills. Online solutions are available for instructors, alongside discipline-specific homework problems across the sciences and engineering. Table of Contents Part I. Getting Basic Tasks Done: 1. Prologue: Preparing to Program 2. Python as a Basic Calculator 3. Python as a Scientific Calculator 4. Basic Line and Scatter Plots 5. Customized Line and Scatter Plots 6. Basic Diagnostic Data Analysis 7. Two-Dimensional Diagnostic Data Analysis 8. Basic Prognostic Modeling 9. Reading In and Writing Out Text Data 10. Managing Files, Directories, and Programs Part II. Doing More Complex Tasks: 11. Segue: How to Write Programs 12. n-Dimensional Diagnostic Data Analysis 13. Basic Image Processing 14. Contour Plots and Animation 15. Handling Missing Data Part III. Advanced Programming Concepts: 16. More Data and Execution Structures 17. Classes and Inheritance 18. More Ways of Storing Information in Files 19. Basic Searching and Sorting 20. Recursion Part IV. Going From a Program Working to Working Well 21. Make it Usable to Others: Documentation and Sphinx 22. Make it Fast: Performance 23. Make it Correct: Linting and Unit Testing 24. Make it Manageable: Version Control and Build Management 25. Make it Talk to Other Languages.