Deep Learning: Java Language Implementation

Name: Deep Learning: Java Language Implementation
ISBN: 9787111572985

Sugako YusukeChina Machine Press2017

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Chapters: 10
Series: Intelligent Systems and Technologies Series
ISBN: 9787111572985

Summary

This book is an introductory guide to deep learning written by Japanese technology expert Yusuke Sugomori for Java developers. The Chinese edition was translated by Chen Peng, Wang Lei, and Lu Ming and published by China Machine Press in 2017 as part of the Intelligent Systems and Technologies Series; the original English edition is Java Deep Learning Essentials, published by Packt. The book has eight chapters. Chapter 1 reviews the evolution of artificial intelligence and machine learning and explains the background and significance of the rise of deep learning. Chapter 2 lays the groundwork for deep learning, introducing supervised and unsupervised learning, support vector machines, hidden Markov models, logistic regression, multilayer perceptrons, and other classic algorithms together with their training procedures. Chapter 3 discusses the revival of neural networks, analyzing the principles and implementation of restricted Boltzmann machines, deep belief networks, denoising autoencoders, and stacked denoising autoencoders. Chapter 4 focuses on deep learning methods that do not rely on pretraining, including dropout and the convolution and pooling operations of convolutional neural networks. Chapter 5 turns to engineering practice, introducing the installation and configuration of ND4J and DL4J and using the MNIST example to demonstrate model construction and learning-rate tuning. Chapter 6 discusses application hotspots such as image recognition and natural language processing as well as the real-world challenges of deep learning. Chapter 7 compares frameworks such as Theano, TensorFlow, and Caffe. Chapter 8 offers an outlook on the direction of artificial intelligence. Drawing on his own engineering experience in products such as news recommendation, the author presents mathematical principles alongside concise Java code, aiming to help readers start from zero and gradually acquire the ability to solve deep learning problems with Java. The book is concise, at about 188 pages, and serves well as a Java engineer's first practical guide into deep learning.

Editor's review

市面上大多数深度学习书都以Python为主体，Java开发者在转向AI时常常面临“语言不通”的困境。这本书正是针对这一痛点而写：它不假设你懂Python，也不要求你有GPU集群，而是从机器学习的基本概念讲起，用Java代码把深度信念网络、自编码器、dropout、卷积神经网络一路实现下来，最后落到DL4J与ND4J这两个Java生态的深度学习框架上。作者巣笼悠辅是日本人工智能创业公司MICIN的联合创始人兼CTO，也曾在东京大学担任客座讲师，做过基于机器学习的新闻推荐产品。书中不少关于调参、学习率优化、模型落地的讨论，带有产品一线的实际感受，而非单纯的理论复述。翻译上由陈澎、王磊、陆明合作完成，术语沿用国内通行译法，代码与公式保留原貌。需要提醒的是，本书篇幅只有约188页，是“入门＋落地”的精炼读本，而非大全式手册；DL4J相关版本也停留在2017年前后。若你希望在较短时间里理解深度学习的主要算法脉络，并在Java工程中真正跑起来，这本小书足以充当一块扎实的跳板。

Who it's for

A good fit for

掌握Java基础语法、想用熟悉语言进入深度学习领域的后端开发者
需要把模型部署进Java企业系统、关注工程落地的软件工程师
对神经网络原理与手写实现感兴趣、希望绕开Python生态的学习者
计算机相关专业学生，可作为机器学习课程的辅助实践读物

Not a good fit for

完全没有编程基础、也未接触过线性代数与概率论的读者，起步会较吃力
希望系统学习Transformer、大模型等前沿架构的读者，本书内容止于CNN与早期框架
只使用Python生态（PyTorch/TensorFlow）且无Java开发需求的数据科学从业者

Reading tips

第1至2章可快速通读以建立概念地图，把重点精力放在第3至5章的算法推导与代码实现上。
边读边动手：先把DL4J与ND4J环境配好，用MNIST示例跑通一个最小模型，再回头对照书中的公式理解每一层在做什么。
第6至7章可作为框架选型的参考资料，阅读时结合DL4J官方文档补齐书中未覆盖的版本更新与API变动。
若数学基础薄弱，可同步补一补矩阵运算与梯度下降，避免在第3、4章卡壳。

About the publisher

China Machine Press China · Founded in 1950

In addition to science and technology and professional publishing, the books in our catalog from China Machine Press also reflect a sustained focus on psychology, computing, management, finance and economics, and social sciences. In psychology, the catalog includes works by C. G. Jung and introductions to analytical psychology, as well as applied topics such as stress and health, and addiction and healing. In computing, it features Structure and Interpretation of Computer Programs (JavaScript Edition). Management and finance titles include Administrative Behavior (4th Edition), The Price of Inequality (Collector's Edition), and This Time Is Different: Eight Centuries of Financial Folly (Commemorative Edition). In the philosophy and history of science, there is The Worldview: The Third Edition. Relevant authors include C. G. Jung, William McGuire, Sonu Shamdasani, and Gabor Maté. Public information is limited; the above summarizes the publisher's profile based solely on the books in our catalog.

Translator's Preface
Foreword
Chapter 1 Overview of Deep Learning (The Evolution of Artificial Intelligence; The Evolution and Limits of Machine Learning; Distinguishing Humans from Machines; Artificial Intelligence and Deep Learning)
Chapter 2 Machine Learning Algorithms: Preparing for Deep Learning (Training Requirements; Supervised and Unsupervised Learning; SVM; HMM; Logistic Regression; Reinforcement Learning; Single-Layer and Multilayer Perceptrons)
Chapter 3 Deep Belief Networks and Stacked Denoising Autoencoders (The Decline and Revival of Neural Networks; Pretraining; Restricted Boltzmann Machines; DBN; Denoising Autoencoders; SDA)
Chapter 4 Dropout and Convolutional Neural Networks (Deep Learning Without Pretraining; Dropout; Convolution; Pooling; Formulas and Implementation)
Chapter 5 Exploring Java Deep Learning Libraries: DL4J, ND4J, and Others (Implementing from Scratch vs. Using Frameworks; ND4J Implementation; DL4J Setup and Construction; CNNMnistExample/LenetMnistExample; Optimizing the Learning Rate)
Chapter 6 Practical Applications: Recurrent Neural Networks and More (Image Recognition; Natural Language Processing; Challenges Facing Deep Learning; Methods to Improve Model Performance)
Chapter 7 Other Important Deep Learning Libraries (Theano, TensorFlow, Caffe)
Chapter 8 Future Outlook (Industry Trends; Next Steps; Learning Resources)

FAQ

这本书的英文原版是哪一本？

原书为Packt Publishing出版的《Java Deep Learning Essentials》，作者巣笼悠辅，本书经授权由机械工业出版社引进，陈澎、王磊、陆明翻译，2017年7月出版。

零Java基础能读吗？

不太建议。书中代码以Java编写，默认读者熟悉类、接口与构建工具；完全没有Java基础的话，建议先补语法与Maven/Gradle构建流程，再回来阅读第5章的DL4J示例。

书中使用的DL4J版本现在还能用吗？

本书成书于2017年，DL4J与ND4J的API此后有较大变化。学习算法思路与代码结构仍有效，但实际动手时请以DL4J官方最新文档为准，注意依赖版本与示例代码的差异。

这本书讲了大模型或Transformer吗？

没有。本书内容涵盖DBN、自编码器、dropout、CNN，以及Theano、TensorFlow、Caffe等早期框架的横向介绍，属深度学习经典算法阶段，不涉及Transformer与当代大语言模型。

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About the author

Sugako Yusuke Author

Sugako Yusuke is an author about whom limited public information is available, and what can be confirmed centers on a single work in computer technology. The author's representative work is Deep Learning: A Java Language Implementation, a title in the categories of computing, artificial intelligence, and programming design, aimed at readers who wish to understand and practice deep learning with the Java language. Judging from the subject matter, the author focuses on combining the fundamentals of deep learning with a specific programming language, leaning toward engineering practice and code-level implementation rather than purely theoretical discussion. This approach generally emphasizes hands-on applicability, making it easier for developers with a Java background to follow the examples and build and run them step by step. Since only one work by this author is held in the collection, details such as birth and death dates, nationality, and professional background cannot be verified, and any assessment of the author's writing style and overall influence must remain limited to this single book rather than being extended further.