Autumn comes, the temperature drops, small friends remember to add clothes, to be careful of cold. Autumn in October, cool autumn mood, busy study, busy work, pay attention to rest.
Xiaobian recommended 7 new books on the shelves of programmers, if you like it, please bookmark it.
1. Python transfer learning
By Dipanjan Sarkar
Translator: Zhang Haoran
This book will help you understand the key concepts of machine learning and deep learning, and describe important deep learning architectures, including convolutional neural networks, deep neural networks, recursive neural networks, short and long memory neural networks, and capsule networks. Through reading this book, the reader will have a deep understanding of the concepts related to transfer learning, and master model freezing, model tuning, and pre-training models (including VGG, Inception, and ResNet models) based on relevant programming cases. This book focuses on a wide range of real-world cases and problems in different fields, such as computer vision, audio analysis, and natural language processing.
Transfer learning is a machine learning technique that can gain knowledge from training a series of machine learning problems and apply that knowledge to training other similar types of problems. The book is divided into three parts: The first part is the foundation of deep learning, which introduces the basic knowledge of machine learning, the basic knowledge of deep learning and the architecture of deep learning; The second part is the essence of transfer learning, which introduces the basic knowledge and power of transfer learning. The third part is a case study of transfer learning, which introduces image recognition and classification, text document classification, audio event recognition and classification, DeepDream algorithm, style transfer, automatic image scan generator, image coloring and so on. This book is for data scientists, machine learning engineers, and data analysts, as well as readers interested in machine learning and transfer learning. Before reading this book, readers are expected to have a basic understanding of machine learning and Python programming.
2. Programming Method (2nd Edition)
The author: Matthias Felleisen, Robert Bruce Findler, Matthew Flatt, Shriram Krishnamurthi
Translator: Zhu Chongkai
- By Matthias Felleisen, the world-renowned computer scientist and creator of the PLT Scheme (Racket) language
- Teach systematic programming methods
This book focuses on the process of programming, presents guidelines for programming, and shows readers how to analyze problem statements, write concise statements of purpose, illustrate examples, develop a framework for a solution, complete a program, and test a program. Because the focus of learning programming is on studying principles and acquiring general skills, this book does not use off-the-shelf industrial programming languages. Instead, it provides a customized educational programming language. For the same reason, DrRacket, a programming environment for beginners, is entertaining and focuses on teaching feedback. As readers become familiar with the book, the programming environment will evolve until it can support a mature language for all programming tasks. The second edition has been thoroughly revised. While this book is still teaching systematic programming methods, edition 2 provides different design tips for graphical interactive programs and batch programs. In addition, version 2 adds many new tips on how to design functions. The instructional language used in this book and its integrated development environment (IDE) can now support images as well as numbers, and support testing, event-driven programming, and even distributed programming.
3, analysis mode: reusable image mode
By Martin Fowler
Translator: Zhong Jing
This book makes an important contribution to the growing literature on patterns. It extracts sophisticated object modeling techniques from different fields and generalizes them into a series of patterns. These domain patterns will help you solve challenging modeling problems that span different domains. Erich Gamma, Head of Object Technology, IFA Consulting Offers us the answer, not just the process of getting it. This way, you can look beyond the plain writing to find the substance to build your next business object model. — Ward Cunningham, Cunningham & Cunningham
Rather than presenting abstract theories through simplified cases, this book presents complex models directly from real projects and generalizes in “patterns”. This book not only includes the patterns themselves, but also reflects the author’s extensive practical experience and deep insights into modeling techniques. The book mainly consists of two parts: the first part introduces the “analysis model” from people and organizational management, observation and measurement, inventory and accounting, planning, financial derivatives trading and other business areas; The second part presents a set of “supporting patterns” for implementing analysis models into specific software. Experienced architects, business analysts, and senior developers can quickly apply the patterns and ideas in this book to practice and improve their development. While beginners to software development may not be able to immediately grasp all of this book, it is enough to open a window to a new world and lay the foundation for future improvement.
4. Docker Practice (Version 2)
By Ian Miell
Translator: Yang Rui, Wu Jiaxing, Liang Xiaoyong, Huang Bowen
Provides plenty of practical advice for applying Docker to the problems users are currently experiencing. — Ben Firshman, Docker, from the preface to the first edition of this book full of high level techniques! — Chad Davis, SolidFire You’ll love Docker after reading this book. Kirk Brattkus, Net Effect Technologies — Some Docker tips for the full software development industry
This book explains the related content of Docker from simple to profound, covering the whole landing process and relevant practical skills from development environment to DevOps assembly line and all the way to production environment. The book introduces the core concepts and architecture of Docker, as well as the method of organically and efficiently combining Docker with the development environment, including using back Docker as lightweight virtual machine, building container, host machine arrangement, configuration management, thin image, etc. Moreover, in the form of “problem-solution-discussion”, the book disassembles a series of difficult problems such as how to integrate Docker into DevOps assembly line and how to land in production environment into 114 relevant practical skills, providing readers with solutions and practical experience in some details and skills. Reading this book, readers will learn not only Docker, but also front-line production experience in continuous integration, continuous delivery, build and image management, container choreography, and related areas. Docker 1.13 was the version of Docker that some of the cases referred to when this book was written.
This book requires readers to have some basic knowledge of container management and operation and maintenance. It is suitable for relevant technical personnel who want to put Docker into practice, especially for readers with middle and advanced DevOps and operation and maintenance background.
5. Artificial intelligence and big data
By Anand Deshpande
Translator: Zhao Yunfeng, Huang Weizhe
- Artificial intelligence and big data technology
- A complete guide to implementing big Data automation solutions using ARTIFICIAL intelligence
The book is divided into two parts, a total of 12 chapters. Chapter 1 to Chapter 5 introduces the ontology of big data and the basic theory of machine learning, laying a foundation for the practice of specific scenarios and algorithms. Readers can learn how the processing and transformation of big data in engineering practice is similar to the process of learning knowledge and translating it into practice. In the introduction to machine learning, basic explanation of its mathematical principles and training process will be given, and codes will help readers understand the use of technical tools in real scenarios. Chapters 6 through 12 provide a number of different use cases, independent of each other, that describe how to implement big data automation solutions using artificial intelligence technologies (natural language processing, fuzzy systems, genetic programming, swarm intelligence, reinforcement learning, network security, cognitive computing). If the reader has some knowledge of the Java programming language, distributed computing frameworks, and various machine learning algorithms, this book can help you build a holistic view of the application of ARTIFICIAL intelligence technology to big data from a broader perspective. If readers know nothing about the above knowledge, but are very interested in the technology and business of big data ARTIFICIAL intelligence, then they can get a cognitive improvement from zero to one through this book.
6. Artificial intelligence Practice Record
Authors: China Electronic Information Industry Development Institute (CCID Research Institute), Artificial Intelligence Industry Innovation Alliance
Editor: Wu Xiaoyan
Ai technology is moving faster and faster from the research stage to the application stage. The book showcases new AI technologies being used in various industries. This shows the rapid development of Chinese enterprises in the rapid creation of AI basic technology platforms, AI application platforms and personal AI applications. The book is well structured, easy to understand and well worth reading. Hans Uskelt, Member of the European Academy of Sciences and Scientific Director of the German Research Center for Artificial Intelligence
How AI can truly create value in the vertical field is an important direction that countries around the world are exploring. The practical examples in this book are a good start for exploring the field of AI. — Zhang Jianwei, member of the Hamburg Academy of Sciences, Professor of Informatics Science at the University of Hamburg, And Distinguished Visiting Professor at Tsinghua University
The key to the development of artificial intelligence is to maximize the value of scientific and technological innovation through application and practice. “Artificial Intelligence Practice Record” collected and analyzed nearly 40 practical cases, to facilitate industry exploration and innovation landing, for promoting the development of Artificial intelligence in China has a very positive significance. — Desheng Lee, Managing director of Innovation, Intel China
The book is divided into three parts, which are overview, general technology and industry application. The review introduces the development of ai products and ai policy environment at present. The general technology section carefully selects the products of 10 enterprises whose core competitiveness is to research and develop the underlying technology, and introduces their implementation ideas and current applications in detail. There are 24 cases in the industry application section, which mainly collects application cases of the combination of ARTIFICIAL intelligence technology and real economy, focusing on the expansion of application scenarios of artificial intelligence technology.
7. Machine learning promotion theory and algorithm
By Robert E. Schapire and Yoav Freund
Translator: Sha Ying
This book is an excellent mental stretcher and deserves to be read well and reread many times, even by non-specialists. – the ACM Computing Reviews
Suffice it to say, this is one of the best books I’ve ever read on machine learning… – Bactra comments
For those wishing to work in the field of machine learning, this book provides a clear and insightful perspective that deserves a place both in the machine learning Canon and on the shelves of researchers. — Giles Hooker, American Statistical Association
The lifting method proposed by Robert Schapire and Yoav Freund has had a huge impact on machine learning and statistical learning, and it has stood the test of time. There is heated debate about why ascension works so well, and the jury is still out. This well-balanced book from the Master covers all aspects of ascension research and helps readers quickly enjoy the richness of research in the field. — Trevor Hastie, Department of Statistics, Stanford University
Written by the authors of ascension, Robert E. Schapire and Yoav Freund, this book aggregates, organizes, simplifies, and substantially expands research on ascension, The theory and practice of ascension are presented in a way that can be easily read and understood by readers of all backgrounds, while providing an authoritative reference for senior researchers. All the material is tailored to the needs of the beginning reader, with exercises at the end of each chapter, making this book suitable for use as a relevant textbook.
This book first gives a brief introduction to machine learning algorithm and its analysis methods. Then the core theory of the method of ascension, especially its generalization ability, is discussed. A number of theoretical perspectives that contribute to the understanding and interpretation of the method of ascension are examined. It provides practical extensions to the lifting method to solve more complex learning problems. Finally, some advanced theories are put forward. Numerous application examples and illustrations are provided throughout.
This book is suitable for anyone who is interested in machine learning algorithms and ascension, as well as for higher education courses.
Coming soon:
Artificial Intelligence Algorithms (Volume 2) : Algorithms inspired by Nature
Full color printing, examples to explain easy to understand the basic artificial intelligence algorithm. Rich sample code and online resources for hands-on practice and extended learning
The book introduces algorithms that offer solutions in artificial intelligence scenarios, involving crossover and mutation, genetic algorithms, particle swarm optimization, cellular automata, and more — all inspired by genes, birds, ants, cells, or trees. Although the algorithm is inspired by nature, you don’t have to know anything about biology to read this book. The “Algorithms for Artificial Intelligence” books are aimed at people who are interested in ARTIFICIAL intelligence but do not have a good foundation in mathematics. A basic understanding of college algebra is required, and complex formulas from calculus, linear algebra, and differential equations will be introduced when necessary. This book provides the reader with the accompanying sample program code, currently available in Java, C#, Python, and Scala versions.
Tao Bending – CMMI 2.0 Practice Guide
It is good to see that the author and his team have compiled and published this book based on years of practical experience. According to the author’s experience in implementing, consulting and evaluating software process improvement for many years, the author explains the essence of CMMI 2.0 with cases, and provides an effective way for enterprises to smoothly transition from CMMI 1.3 to CMMI 2.0. I believe that this book can help software enterprises to avoid detachments and effectively implement software process improvement, especially the “practice focus” part, which is of great practical guiding significance. , senior director of CMMI appraiser Yu Jun Ann Book is not only the latest international standard of the r&d management commands, a line of Ming, tell us the height of the mature software enterprises should match, and combined with the practice of the author for many years a line consulting experience, gives some ways to improve r&d management and technology, a simple, dry, full It’s a very useful guide to action. — Liu Yongting, deputy director of Zhuowang Information R&d Center of China Mobile
This book systematically interprets the practices in THE CMMI 2.0 model. First, it introduces the changes of CMMI 2.0 relative to CMMI 1.3, and clarifies the core ideas and concepts of CMMI 2.0. Then, it gives a popular, detailed and casealized interpretation of each practice field in the CMMI model. Finally, a systematic comparative analysis is made between agile methods and CMMI model, and the complementary integration of the two is advocated. In this book, the interpretation of CMMI 2.0 model is easy to understand, concise and comprehensive, and a large number of practical application cases are given, which can help readers quickly and accurately grasp the meaning of the model, and map and combine with their own actual scenarios, and flexibly realize the requirements of the model. This book is suitable for senior managers, project managers, process improvement personnel, quality management personnel, agile coaches, consultants and r&d personnel involved in engineering practice.