1, mathematics of machine learning
The list of new books in a week is second. Books that all machine learning engineers should read.
Douban comments:
A good book about the basics of machine learning.
Machine learning has been on fire for several years, and with the development of 5G and computer computing power, this popularity can last for a long time, such as big data, Internet of Everything, deep learning, computer vision and so on. Everyone wants to know about machine learning and get a piece of it, but it is much more difficult for human beings to learn machine learning, because advanced mathematics knowledge is a hurdle that everyone who deeply studies machine learning can't bypass.
This book is to solve this problem. Starting with basic calculus, linear algebra and probability theory, it extends to optimization method, stochastic process and graph theory, and connects with the machine learning algorithms they apply, laying a good foundation for the introduction of machine learning. Therefore, people who want to thoroughly understand machine learning are strongly recommended to read this book. Moreover, this book is easy to understand and is really a good choice for beginners.
The combination of theory and practice can be targeted by both beginners and experienced people.
Although the content of this book is basically mathematical knowledge, it actually attaches great importance to the combination of theory and practice. While explaining mathematics knowledge, it also illustrates its practical application in machine learning with examples, and comes with Python code, so that mathematics is no longer pure mathematics, but the door of machine learning application. . Beginners can learn from mathematics, algorithms and codes along the contents of this book, while readers with the basis of machine learning algorithms can read the applications first, and then read the mathematics content in each application accordingly, so as to make the book play its greatest role.
2. Practical Guide to Agile Testing
From theory to practice, comprehensively explain the knowledge of software testing in microservice and agile mode. Yun Yun's Z new work, recommended by experts such as Kerry Zhu and Ru Bingsheng, provides complete code and containerization technology. This book mainly introduces the process, method and technical practice of agile testing. Based on the current mainstream agile system practice, this book starts from the user story diagram, gradually combs the iterative process, builds an iterative delivery plan, builds a continuous integration and continuous release pipeline for the R&D domain, thus developing characteristic branches, further completing the mainstream micro-service architecture coding and layered automation system construction, supporting containerized management and maintenance systems, and finally finishing the knowledge system combing of the whole delivery life cycle. This book allows readers to clearly and completely understand the end-to-end process under the whole agile testing process, thus expanding their horizons, gradually improving their testing awareness and ability, and meeting the full-stack requirements of agile testing.
This book is suitable for testers, test managers and programmers, and can also be used as a learning book for teachers and students of related majors in colleges and universities and a teaching material for training schools.
3. Zero-based machine learning
The artificial intelligence tutorial book briefly introduces the neural network and deep learning based on python framework algorithm, and the classroom training of Xiaobaishu is equipped with rich practical cases, giving examples of readers' comments on the source code of the whole book.
A particularly good book. I read it as a novel after I bought it, and I even couldn't put it down before going to bed. The author writes machine learning vividly, which is really easy for people to understand. Because I am so absorbed in reading, I can even dream that I have become a machine learning master of Cheng. I wonder what kind of person the author is. He must be a humorous and elegant siege lion. I have carefully studied the author's portrait, and I feel deja vu. The sound and appearance on the portrait are as real as you, me and him around us. After learning this book, I think I have a good plan for the interview of AI company. I wish myself an ideal job as soon as possible! ! !
This book has passed AI? Xiaobai? The dialogue of "Xiao Bing's Apprentice Programmer Kago Learning Machine Learning" is simple and practical, mainly including the fast learning path of machine learning, the basic knowledge of mathematics and Python, the basic algorithms of machine learning (linear regression and logical regression), deep neural network, convolutional neural network, circular neural network, classical algorithm, integrated learning, unsupervised and semi-supervised learning, actual combat of reinforcement learning, and related practical cases. All the cases in this book are realized by Python, Scikit-learn machine learning library and Keras deep learning framework, and also contain rich data analysis and data visualization.
This book is suitable for programmers, project managers, college students and anyone who wants to learn machine learning from zero basis, thus introducing the field of machine learning and establishing a knowledge channel from theory to actual combat.
Proficient in Rust 2 nd edition
Reader's comments
Very good, unlike other books in China, rust, this book especially emphasizes operability! Examples are good! Penetration test! !
This book is very good. It's not a detailed introduction to grammar, just a preliminary impression. Instead, it focuses on related tools and development processes.
Save the details for later.
The content of this book is * * * Chapter 17, which explains the related knowledge of Rust from simple to deep, involving basic grammar, package manager, testing tools, type system, memory management, exception handling, advanced types, concurrency model, macro, external function interface, network programming, HTTP, database, WebAssembly, GTK+ framework and GDB debugging.
This book is suitable for readers who want to learn Rust programming. I hope readers can know something about C, C++ or Python. Rich code examples and detailed explanations in the book can help readers get started quickly and master Rust programming efficiently.
Books worthy of repeated recommendation
Code neatness
Douban review
Everyone who writes code should read this book. Although the example in the book is Java and I use Python, the requirements for good code are the same no matter what language I use.
Usually when writing code, I will pay attention to naming, format and other issues, but I just grope for it myself. Some places seem to understand, as if they are separated from the truth by a layer of yarn. And this book just helped me to pierce this veil and establish my basic understanding of good code. After reading it, I will also pay attention to the main points mentioned in the book when writing code.
Become a better programmer
R talked about the new company before leaving his job, and told me that he had not joined the company yet. The project manager asked him to read the book Code Cleanliness. I've heard of it. Ha ha ha smile. I know this book, but I have never read it.
Everything is related to the code, and everything starts with the code. So, more than ten years ago, I knew the importance of high-quality code, and I also understood that the output of excellent programmers is far greater than that of ordinary programmers. I also have several books about code on my desk, such as Refactoring, such as The Art of Writing Readable Code. Some principles are very general, but I was shocked by the contents of this CleanCode, which is simply full of words.
Refactoring to improve the design of existing code (second edition paperback edition)
Douban review
Classic reprint open code, huh? Javascript？ ! Compared with the first version of Java code, the second version can be said to be full of sincerity. In order to keep pace with the times, many contents have been modified. Even the language used has changed. Chapter 6-12 is the essence of this book, which is extremely valuable (just look at the sample code to understand the core meaning).
This book tells why and how to improve the design of existing code. The first chapter is as bad as ever, and the second chapter is average. However, chapters 3, 5, 6 to 12 that can be browsed quickly are quite valuable. The second edition of this book is rewritten in JavaScript, but like the first edition, I don't recommend reading the code inside, which is too redundant to tell the actual situation of the readers. We should start from the actual code we have processed, read the motivation and practice of the technology proposed in Refactoring, and try it in practice. This is the best way to use this book. The meaning of refactoring is not to really master any skills, but to think about your work and your code.
Which edition of this book have you read?
Another book by MartinFowler, the world software development master, is also very classic.
Typical methodology books only focus on tools and techniques, and the object-oriented community expects a book to break through this limitation, and this groundbreaking book just meets this demand. In this book, the author focuses on the final result of object-oriented analysis and design, that is, the model itself. In this book, the author shares his rich experience in object modeling and his keen insight into identifying repetitive problems and transforming them into reusable models, and gives a series of models from different fields (including transactions, measurement, accounting and organizational relations, etc.). ).
Conceptual patterns cannot exist in isolation. Based on this understanding, the author also gives a series of? Support mode? . These patterns discuss how to transform the conceptual model into software and make it suitable for the architecture of large information systems. The explanation of each pattern includes the design idea behind it, when to use these patterns (or not) and the know-how in implementation. The examples shown in this book constitute a practical manual, which not only contains useful models, but also contains profound insights into reuse techniques, which is helpful to improve analysis, modeling and implementation.
What have you learned? Welcome to comment ~
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