Xiaobian has not recommended the list of new books for a long time. After a closer look today, machine learning and deep learning books account for most of the new books. It was the focus of last week’s list of new books, so I felt compelled to share it with you. Please read it carefully. Sort according to the list.

How does MySQL run

• An in-depth analysis of the MySQL database performance will be required to run data analysis and data processing books
• More than 200 pictures are used to help explain the key content, provide color map download, public number q&A service, two-color printing.

Why is this SQL statement executing so slowly? Why do I create an index, but the query plan does not show up? Why do not use indexes when there are many parameters IN the IN query? Why does my data appear to be garbled? … Every DBA and back-end developer will encounter these issues at some point or another when dealing with MySQL. In addition, index structure, MVCC, isolation level implementation, the use of locks, and other knowledge are also common questions in MySQL interviews. Written in a humorous and easy to understand style, this book provides solutions to these questions. Although the presentation of this book is very different from the usual academic and theoretical IT books, IT is a very serious technical book, covering some of the core concepts that students using MySQL see in job interviews and in the workplace. This book is an excellent book for technical personnel who are MySQL experts, DBA whose skills need to be further improved, and even those who are new to the database industry to thoroughly understand how MySQL works.

Natural language processing (NPL) uses Python to understand, analyze, and generate text

• Python Natural Language processing NLP to get started
• A practical guide for practitioners in the field of modern natural language processing
• Source code provided, translated by NLP team of Xiaomi AI Lab

Because deep learning and NLP are inseparable, courses and books on “deep learning +NLP” have been popping up in recent years. This book is one of them. Like other practice books, this book has both basic theory and programming practice, the basic theory part is simple and easy to understand, programming practice part can be directly downloaded source code run, this collocation is particularly suitable for beginners, can be used as the first introduction of modern NLP practitioners.

In this book, readers will learn not only the inner workings of these systems, but also relevant theoretical and practical skills and create their own algorithms or models. Basic computer science concepts are seamlessly translated into a solid foundation of methods and practices. Starting with some time-tested classical methods (such as TF-IDF) and moving on to the deep neural networks associated with NLP, the author takes the reader on a clear experience tour of the core methods of natural language processing.

PyTorch generates anti-network programming

• Hands-on deep learning neural network and deep learning, image recognition construction GAN convolution image generation
• Supporting example code, illustrated, PyTorch to build your own generative adversarial network.

Generative Adversarial Network (GAN) is a new phenomenon in the field of neural networks and has been hailed as “the coolest idea in machine learning in the last 20 years.”

This book introduces the reader to generative adversarial networks in a straightforward, short way and teaches the reader how to write generative adversarial networks in a step-by-step manner using PyTorch. The book consists of three chapters and five appendices, which respectively introduce the basic knowledge of PyTorch, the development of neural network with PyTorch, the improvement of neural network to improve the effect, the introduction of CUDA and GPU to accelerate GAN training, and the generation of high quality image convolution GAN, conditional GAN and other topics. The appendix covers topics that have been neglected in many machine learning-related tutorials, including calculating ideal loss values for balanced GAN, probability distributions, and sampling, and how convolution works. It also briefly explains why gradient descent is not suitable for adversarial machine learning. This book is for readers who want to learn a little about gans and how they work, as well as for machine learning practitioners who want to learn how to build gans. For students who are taking machine learning courses, this book can help readers get started quickly and lay the foundation for subsequent learning.

4. Introduction and practice of machine learning testing

• The industry’s first AI testing work, 32 BAT experts jointly recommended
• Selected 15 AI testing points covering five major technical topics
• Quick start machine learning tests from scratch. Rong 360AI test team

This book comprehensively and systematically introduces machine learning testing technology and quality system construction, which is divided into 5 parts and 15 chapters. The first part (chapters 1-4) covers the basics of machine learning, Python programming, and data analysis. The second part (Chapter 5 ~ 7) introduces the basis of big data, big data testing guidelines and relevant tool practices; The third part (Chapter 8 ~ 10) explains the machine learning test foundation, characteristic special test and model algorithm evaluation test; The fourth part (Chapters 11 ~ 13) introduces the model evaluation platform practice, machine learning engineering technology and continuous delivery process of machine learning. The fifth part (Chapters 14 and 15) discusses the practice of AI (Artificial Intelligence) in the field of testing and the future of test engineers in the ERA of AI. This book helps readers understand how machine learning works and how quality assurance works in machine learning. By reading this book, engineering developers and test engineers can systematically understand the knowledge of big data testing, feature testing and model evaluation. Through reading this book, algorithm engineers can learn the method of model evaluation and broaden the ideas of model engineering practice; By reading this book, technical experts and technical managers can understand the construction of machine learning quality assurance and engineering performance.

Analysis patterns: Reusable object models

• World class software developer Martin Fowler’s classic works come alive!
• The authors share extensive experience in object modeling and a keen insight into identifying repetitive problems and turning them into reusable models
• A practical manual explaining patterns in different fields, including transactions, measurement, accounting, and organizational relations

Typical methodological books focus only on tools and techniques, and the object-oriented community is looking for a book that breaks that mold, and this groundbreaking book fits that mold. In this book, the author focuses on the end result of object-oriented analysis and design, the model itself. The authors share their extensive experience in object modeling and their keen insight into identifying repetitive problems and turning them into reusable models, along with a range of patterns from different domains including transactions, measurement, accounting and organizational relations. Based on the realization that conceptual patterns cannot exist in isolation, the author also provides a series of “supporting patterns.” These patterns explore how conceptual models can be translated into software and adapted to the architecture of large information systems. The explanation of each pattern covers the design thinking behind it, when you should (or shouldn’t) use the patterns, and the tricks of the trade in implementing them. The examples presented in this book form a practical manual that includes both useful models and deep insights into reuse skills that help improve analysis, modeling, and implementation.

6. Python migration learning

• Advanced deep learning and neural network models are implemented using TensorFlow and Keras
• The Complete Guide to Deep learning and Transfer Learning (Learning from Scratch)

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.

7. Python image processing

• Image processing, computer vision face recognition image repair
• Introduction to Programming tutorial books Zero basics, deep learning crawlers, solving image processing problems with popular Python image processing libraries, machine learning libraries and deep learning libraries

This book describes how to use popular Python image processing libraries such as PIL, Scikit-Image, Python-OpencV, SciPy Ndimage and SimpleITK), machine learning libraries (Scikit-learn), and deep learning libraries (TensorFlow, Keras) solve image processing problems. Through this book, readers will be able to write program code to realize complex image processing (such as image enhancement, filtering, restoration, segmentation, classification and object detection), and master the method of solving complex image processing problems using machine learning and deep learning models.

The book starts with the basics and guides the reader up the ladder through the Python reproducible implementations provided in the book. Starting with classical image processing techniques, the book explores the evolution of image processing algorithms to the latest advances in image processing or computer vision and deep learning.

8, HTTP packet capture interface automation test

• “HTTP packet capture actual combat” the author of a new interface automation test
• Master Fiddler packet capture and JMeter packet issuance, Cookie mechanism, hijacking attack test Web debugging, front-end development engineer reference book

The book is divided into 30 chapters, each chapter is not much, but with vivid and interesting examples and lots of pictures for readers to refer to and practice. Readers can learn a chapter quickly and feel a sense of achievement with each chapter.

Chapters 1-11: adds a little knowledge of the HTTP protocol and how to use Fiddler to catch and analyze HTTP packets.

Chapters 12 through 22 show how to send HTTP packages through JMeter, Postman, and Python+ Requests for automated testing of software and interfaces.

Chapter 23 ~ 26: introduces how to use the packet capture tool to implement security and performance testing by listing many interesting cases.

Chapter 27 ~ 30: using the content described in this book, several comprehensive examples are realized which are widely used in daily life.

Artificial Intelligence Algorithms Volume 2 Algorithms inspired by nature

• AI algorithm introduction tutorial, algorithm introduction, examples to explain the basic algorithm of artificial intelligence!
• Rich sample code and online resources to provide an online experimental environment
• Source code download, convenient hands-on practice and extended learning, full color printing

Algorithm is the core of artificial intelligence technology, and nature is an important source of inspiration for artificial intelligence algorithm. The book introduces algorithms influenced by genes, birds, ants, cells, and trees that provide practical solutions for many types of AI scenarios. The book consists of 10 chapters covering population, crossover and mutation, genetic algorithms, speciation, particle swarm optimization, ant colony optimization, cellular automata, artificial life and modeling. All algorithms in the book are explained with specific numerical calculations, and each chapter is accompanied by program examples for the reader to try out.