Late, finally bid farewell to the hot summer, ushered in a touch of autumn cool. Xiaobian feel a lot of refreshing, you are the same.

At the end of the month and the beginning of autumn, I will introduce you some new books that will be on the shelves or have been on the shelves in August.

 

1. Unsupervised Learning of Python (available in late August or early September)

 

Book through the Python language on unsupervised learning, the book includes chapter 10, the front nine chapters 1 to explain the basics of without supervision and learning, the basic knowledge of clustering, senior clustering, hierarchical clustering, soft clustering and dimensionality of gaussian mixture model, anomaly detection, component analysis, the unsupervised neural network model, the generated type against network and the self-organizing map, Chapter 10 provides solutions to the problems covered in the previous nine chapters in the form of problem solving.

This book requires a basic knowledge of machine learning and Python programming. In order to fully understand all of the theories in the book, you will need to know college-level knowledge of probability theory, calculus, and linear algebra. However, readers unfamiliar with these topics can skip the math discussion and focus on the practical aspects. When needed, you can refer to relevant papers and books to gain a deeper understanding of complex concepts.

This book is for data scientists, machine learning practitioners, and software developers in general. By learning the unsupervised learning theory and Python programming methods introduced in this book, readers can gain valuable reference in business practice.

2. Python Programmer Interview Tips (pre-ordered, expected to be available in a few days)

 

  • From the beginning to the master of Python programming, I carefully selected the questions that the examiner or interviewer might ask, and gave comprehensive and detailed answers
  • A practical guide to navigating Python programmers through technical interviews

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

 

This book is a practical guide to navigating Python programmers through technical interviews. The book consists of 14 chapters, divided into Python basics and Python data structures and algorithms. The book covers an introduction to Python, data types and built-in functions, operators in Python, decisions and loops, user-defined functions, classes and inheritance, files, algorithm analysis and big O notation, array-based sequences, stacks, queues, and double-endided queues, linked lists, recursion, trees, search, and sorting. Along with a brief introduction to Python topics, the book also includes questions that interviewers may ask readers, arranged in chapters to help you learn from the basics.

The book is organized in a very systematic way into two parts: Fundamentals of Python programming and Python data structures and algorithms. Even if you’re good at programming, I suggest waltzes don’t take the first part lightly. Even a small error in programming fundamentals is unacceptable at any stage, so we must pay attention to the basics.

This book will not only give you a better understanding of the basics of Python programming, but also introduce you to specific applications. The book is introduced in simple language, with the goal of explaining the logic behind each concept. Both students and professionals can benefit from this book.

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3. Spark Machine learning (expected to hit shelves in late August and early September)

 

Education is not the learning of existing knowledge, but the training of ideas. — Albert Einstein

This book provides a comprehensive solution to the Apache Spark Machine learning API. It not only introduces the basic knowledge needed to accomplish machine learning tasks with Spark, but also covers some advanced Spark machine learning skills. There are 13 chapters in the book, starting from environment configuration, introducing linear algebra library, data processing mechanism, common strategies for building machine learning system, regression and classification, implementing recommendation engine with Spark, unsupervised learning, gradient descent algorithm, decision tree and integration model, data dimension reduction, text analysis and Spark Steaming use successively. This book is for Scala developers who have mastered machine learning techniques, especially for those without hands-on Spark experience. It is assumed that the reader has a basic knowledge of machine learning algorithms and some practical experience implementing them using Scala. Prior knowledge of the Spark ML library and its ecosystem is not required.

Machine Vision TensorFlow 2: Introduction, Principle and Application

 

  • Python Computer vision tutorial
  • Image and video applications for industrial applications based on TensorFlow2.0
  • Practical case study, rich illustrations, detailed explanation, full color printing, provide source code

The book uses examples as a guide, interspersed with neural network knowledge, and the TensorFlow 2 framework to demonstrate specific projects. The book does not start from scratch, but requires some basic Python knowledge. This book will be easier if you are also familiar with neural networks and the TensorFlow 2 framework.

This book describes the TensorFlow 2 framework and its application to machine vision. The example code in the book is carefully selected by the author. The reader needs to work through specific examples step by step in chapter order. It is recommended that you write the sample code in turn by hand to ensure that you understand what each line of code does.

5. Actual practice of R language medical data analysis

 

  • Introduction to Medical statistics, recommended by Prof. Yu Songlin, Tongji Medical College, Huazhong University of Science and Technology
  • Emphasis on actual combat and application, highlighting the nature of the problem and the overall structure
  • Contains numerous R program examples and graphics to take you to a deeper understanding of data analysis

This book takes medical data as an example to explain how to use R for data analysis. Combined with a large number of selected examples, common analysis methods are introduced in simple terms to help readers solve practical problems in medical data analysis. The book is divided into 14 chapters, chapter 1 to Chapter 3 introduces the basic usage of R language; Chapter 4 covers data visualization; Chapter 5 introduces the basic statistical analysis methods. Chapter 6 ~ 8 introduce three regression models commonly used in medical research. Chapter 9 introduces the basic methods of survival analysis. Chapter 10 ~ chapter 12 introduces several common multivariate statistical analysis methods. Chapter 13 introduces the statistical evaluation indexes and calculation methods of clinical diagnostic tests. Chapter 14 introduces Meta analysis methods commonly used in medical research practice. This book is suitable for undergraduate and graduate students majoring in clinical medicine, public health and other medical related fields. It can also be used as a reference book for students and researchers studying data analysis in other fields. By reading this book, readers will not only learn how to quickly solve practical problems using R and related packages, but also gain a deeper understanding of data analysis.

Natural Language Processing and Computational linguistics

 

  • Written by a senior contributor to the Python open source community, one of the few works in computational linguistics
  • A practical guide to text analysis focusing on technical details, available for download

Modern text analysis is very easy to do using Python and open source tools, so it is necessary to master modern text analysis methods in this age of text data. This book describes how to use natural language processing and computational linguistics algorithms to reason and gain insight into the data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. Tools that use these algorithms are now readily available and available in tools such as Python, Gensim, and spaCy. The book starts with data cleansing and then introduces the concepts of computational linguistics. With that in hand, it’s time to use real language and text, and explore the more complex areas of statistical NLP and deep learning with the help of Python. You’ll learn how to annotate, parse, and model text with appropriate tools, and learn how to use the appropriate framework tools. You’ll also know when to choose a tool like Gensim for a topic model, and when to use Keras for deep learning. This book has a good balance between theory and practice, so you can run your own natural language processing project while mastering the theory. You’ll discover Python’s rich ecosystem of natural language processing tools and enter the interesting world of modern text analysis.

7. Deep learning algorithm and practice

 

  • Code application and theory are combined to establish a complete deep learning knowledge system for readers
  • Explain and application related theory and key algorithm, provide source code

The purpose of this book is to establish a complete knowledge system of deep learning for readers. The book consists of three parts. The first part is the mathematical foundation related to deep learning. The second part is the algorithm foundation of deep learning and related implementation; 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 want to engage in related work. It can also be used as a teaching reference book for relevant majors in colleges and universities.

8. SQL Introduction classic (6th edition)

 

  • Classic SQL introduction book new upgrade,SQL language tutorial complete
  • From Oracle and Microsoft-SQL-Server two implementation point of view
  • Do not miss the SQL beginner basic tutorial

1. A series of books with global sales of more than one million copies, a classic brand built for more than 10 years; 2. New upgrade of classic SQL Primer books, the cumulative sales volume of the last edition is more than 50,000; 3. Each chapter is carefully designed for beginners, with 1 hour for easy reading and learning and 24 hours for thorough mastery of key knowledge; 4. This book adopts intuitive, step by step method, introduces the database structure, object, query, table and other content processing; 5. Readers will learn how to use advanced SQL technologies, including views, transactions, Web connections, and extensions to SQL from Oracle and Microsoft SQL Server. 6. Step-by-step examples guide the reader through the most common SQL tasks. Questions and answers, quizzes and exercises help readers test their knowledge. Note that “tips” and “warnings” point to shortcuts and solutions. The book is as follows:

  • Define valid database structures and objects;
  • Turn original database specifications into logical tables;
  • Edit relational data and tables using DML;
  • Manage database transactions;
  • Write effective, high-performance queries;
  • Classify, summarize, sort, group and adjust data;
  • Use date and time;
  • In the query table, the use of sub-query, combination of multiple queries;
  • Master powerful query optimization techniques……

9. SSM and Spring Boot development practice

 

  • How to develop large enterprise projects with Spring, Spring, MVC, and MyBatis (SSM) based on Java, EE, through extensive source code and project case systems

This book takes Java EE as the main development platform, and systematically explains the method, technology and practice of developing enterprise projects through Spring, Spring MVC and MyBatis (SSM) three frameworks. This book mainly introduces the basic knowledge of Spring, Spring MVC and MyBatis, Spring resource management, how to achieve inversion of control, how to simplify the code through Spring expression language, how to reduce the coupling degree between various parts of the business logic through sectional-oriented programming, how to integrate data layer, Combined with specific cases, it describes how to achieve integration of projects through SSM and Spring Boot. This book is suitable for Java programmers, SSM developers, and Spring Boot developers.