Autumn comes, the book is here, 9 new programmer books to meet you

In the second half of the year, I finally bid farewell to the hot summer and ushered in a touch of autumn coolness. The editor feels that the whole person is much refreshed, so are you guys too.

At the end of the month and the beginning of autumn, I would like to introduce to you some of the new August books that will be on the shelves and those that have already been on the shelves. Please read them carefully!

 

1. Python unsupervised learning (available at the end of August or early September)

 

This book explains unsupervised learning through the Python language. The content of the book includes 10 chapters. The first 9 chapters explain the basic knowledge of unsupervised learning, basic knowledge of clustering, advanced clustering, hierarchical clustering, soft clustering and Gaussian mixture model, anomaly detection, dimensionality reduction and component analysis, unsupervised neural network model, generative adversarial network and self-organizing map. Chapter 10 provides solutions to the problems involved in the previous 9 chapters in the form of problem solving.

This book requires you to have basic knowledge of machine learning and Python programming. In addition, in order to fully understand all the theories in the book, you also need to understand the relevant knowledge of probability theory, calculus and linear algebra at the university level. However, readers who are not familiar with this knowledge can also skip the discussion of mathematics and focus only on the practical aspects. When needed, you can refer to related papers and books for a deeper understanding of complex concepts.

This book is suitable for data scientists, machine learning practitioners and ordinary software developers to read. By studying the unsupervised learning theory and Python programming methods introduced in this book, readers can obtain valuable references in business practice.

2. Interview tips for Python programmers (reserved, it is expected to be available in the next few days)

 

• Python programming from entry to proficiency, the sword refers to the offer, carefully selected questions that the examiner or interviewer may ask, and gave a comprehensive and detailed answer
• A practical guide to guide Python programmers through technical interviews

The book is divided into two parts, as shown in the figure below.

 

This book is a practical guide to guide Python programmers through technical interviews. The book has 14 chapters, divided into two parts: Python basics and Python data structures and algorithms. The content covered in this book includes introduction to Python, data types and built-in functions, operators in Python, decision-making and loops, user-defined functions, classes and inheritance, files, algorithm analysis and big O symbols, array-based sequences, stacks, Queue and deque, linked list, recursion, tree, search and sort. While giving a brief introduction to Python-related topics, the book also adds questions that the interviewer may ask the reader. These questions are arranged in chapters so that the reader can learn from the simplest to the deeper.

The content in this book is organized in a very systematic way, divided into two parts: Python programming basics and Python data structures and algorithms. Even if you are good at programming, I would suggest not to despise the first part of Waltz. At any stage, a small mistake in the basics of programming is unacceptable, so we must pay attention to the basics.

This book not only enables you to have a better understanding of the basics of Python programming, but also introduces related specific applications. The book uses simple language to introduce, the goal is to explain the logic behind each concept. Both students and professionals have benefited a lot from this book.

Content display:

 

 

3. Spark machine learning actual combat (expected to be available in late August and early September)

 

Education is not about learning knowledge, but training ideas. --Einstein

This book provides a comprehensive solution to the Apache Spark machine learning API. It not only introduces the basic knowledge required to complete machine learning tasks with Spark, but also involves some advanced skills in Spark machine learning. The book has 13 chapters. Starting from the environment configuration, it successively introduces linear algebra libraries, data processing mechanisms, common strategies for building machine learning systems, regression and classification, using Spark to implement recommendation engines, unsupervised learning, gradient descent algorithms, and decision trees. And the use of integrated models, data dimensionality reduction, text analysis and Spark Steaming. 
This book is prepared for those Scala developers who have mastered machine learning technology, and is especially suitable for readers who lack Spark practical experience. This book assumes that the reader has mastered the basics of machine learning algorithms and has some practical experience in implementing machine learning algorithms using Scala. But readers are not required to understand the Spark ML library and its ecosystem in advance.

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4. TensorFlow 2 of machine vision: introduction, principle and practical application

 

• Python Computer Vision Tutorial
• Explain the image and video applications of industrial applications based on the new version of TensorFlow 2.0
• Practical cases are well integrated, rich in icons, detailed explanations, full-color printing, and source code provided

This book uses examples as a guide, interspersed with neural network knowledge and TensorFlow 2 framework to demonstrate specific projects. The book does not start with a basic introduction, readers need to have a certain Python foundation. If the reader also understands neural networks and is familiar with the TensorFlow 2 framework, it will be easier to study this book.

This book describes the TensorFlow 2 framework and its application in machine vision. The example codes in the book are carefully selected by the author. Readers need to complete specific examples step by step in the order of chapters. It is recommended that the readers refer to the example code in the book to manually write it again in order to ensure that they can understand the role of each line of code.

5. R language medical data analysis actual combat

 

• Introduction to Medical Statistics, recommended by Professor Yu Songlin, Tongji Medical College, Huazhong University of Science and Technology
• Emphasize actual combat and application, highlight the nature of the problem and the overall structure
• Contains a large number of R program examples and graphics, to take you to a deeper understanding of data analysis

This book uses medical data as an example to explain how to use R for data analysis, combined with a large number of selected examples to introduce common analysis methods in a simple way, to help readers solve practical problems in medical data analysis.
The book is divided into 14 chapters. Chapters 1 to 3 introduce the basic usage of R language; Chapter 4 introduces data visualization; Chapter 5 introduces basic statistical analysis methods; Chapter 6 to Chapter 8 Introduction The three most commonly used regression models in medical research; Chapter 9 introduces the basic methods of survival analysis; Chapters 10 to 12 introduces several commonly used multivariate statistical analysis methods; Chapter 13 introduces the clinical diagnostic test Statistical evaluation indicators and calculation methods; Chapter 14 introduces the Meta analysis methods commonly used in medical scientific research practice.
This book is suitable for undergraduates and graduate students in clinical medicine, public health and other medical related majors, and can also be used as a reference book for students and researchers in other majors to study data analysis. Reading this book, readers can not only master the method of using R and related packages to quickly solve practical problems, but also have a deeper understanding of data analysis.

6. Natural language processing and computational linguistics

 

• Written by a senior contributor in the Python open source community, one of the few works in the field of computational linguistics
• A practical guide for text analysis that focuses on technical details and provides source code download

It is very convenient to use Python and open source tools to perform modern text analysis. Therefore, it is necessary to master modern text analysis methods in this era of text data.
This book describes how to use natural language processing and computational linguistic algorithms to reason about the data you have and gain insights. These algorithms are based on statistical machine learning and artificial intelligence techniques. Tools that use these algorithms are now readily available and can be used in tools such as Python, Gensim, and spaCy.
This book starts with data cleaning, and then introduces related concepts of computational linguistics. After mastering these contents, you can use real language and text, and use Python to explore more complex areas of statistical NLP and deep learning. You will learn how to use appropriate tools to annotate, parse and model texts, and master the use of corresponding framework tools. You will also know when to choose a tool like Gensim as a topic model, and when to use Keras for deep learning .
This book balances the relationship between theory and actual cases, so you can execute your own natural language processing projects while mastering theoretical knowledge. You will discover the rich ecosystem of Python, a natural language processing tool, and enter the interesting world of modern text analysis.

7. Deep learning algorithm and practice

 

• Combine code application and theory to establish a complete deep learning knowledge system for readers
• Explain the theories and key algorithms related to the application, and provide the source code

This book aims to establish a complete deep learning knowledge system for readers. The content of the book consists of 3 parts. The first part is the mathematical foundation related to deep learning; the second part is the algorithm foundation and related implementation of deep learning; the third part is the practical application of deep learning. By reading this book, readers can deepen their understanding of deep learning algorithms and apply them to practical work.
This book is suitable for readers who are interested in deep learning and hope to engage in related work, and can also be used as a teaching reference book for related majors in colleges and universities.

8. SQL entry classic (6th edition)

 

• New upgrade of classic SQL introductory books, SQL language tutorials
• Introduce from the two realization angles of Oracle and Microsoft-SQL-Server
• Not to be missed a basic tutorial for beginners in SQL

1. A series of books with more than one million copies sold worldwide, a classic brand built for more than ten consecutive years; 
2. A new upgrade of classic SQL introductory books, with a cumulative sales of 50,000+ in the previous edition; 
3. The content of each chapter is carefully designed for beginners, One hour of easy reading and learning, and 24 hours of thorough mastering of key knowledge; 
4. This book uses an intuitive and step-by-step method to introduce the processing methods of database structure, objects, queries, tables, etc.; 
5. Readers will master advanced SQL technologies (including Views, transactions, Web connections, and Oracle and Microsoft SQL Server's extension to SQL); 
6. Step-by-step examples guide readers to complete the most common SQL tasks. Questions and answers, quizzes, and exercises help readers check their knowledge. "Attention", "Tips" and "Warnings" indicate shortcuts and solutions. 
The content of this book is as follows:

• Define effective database structure and objects;
• Normalize the original database into logical tables;
• Use DML to edit relational data and tables;
• Manage database transactions;
• Write effective, high-performance queries;
• Classify, summarize, sort, group and adjust data;
• Use date and time;
• Combine tables in the query, use sub-queries, and combine multiple queries;
• Master powerful query optimization skills...

9, SSM and Spring Boot development combat

 

• Based on Java, EE, explain how to develop large-scale enterprise projects through Spring, Spring, MVC and MyBatis (SSM) through a large number of source codes and project case systems

This book uses Java EE as the main development platform, and systematically explains the methods, technologies and practices of developing enterprise projects through the three frameworks of Spring, Spring MVC and MyBatis (SSM). This book mainly introduces the basic knowledge of Spring, Spring MVC and MyBatis, Spring resource management, how to realize the inversion of control, how to simplify the code through the Spring expression language, and how to reduce the coupling between various parts of business logic through aspect-oriented programming. , How to integrate the data layer, combined with specific cases, describes how to implement project integration through SSM and Spring Boot. 
This book suits Java programmers, SSM developers, Spring Boot developers to read.