Data structure is the skeleton of the program, algorithm is the soul of the program.
Algorithms are everywhere in our lives. We count time every morning when we wake up, brush our teeth, wash our face, and eat breakfast so that we won’t be late for work or class. Go to the supermarket shopping, in the case of limited funds, consider what to buy first, what to buy later, calculate whether the excess; Cook at home, what ingredients, spices, practices, steps, but also taste the salty, see if it is done. So don’t say you don’t know algorithms, you use them every day!
But when it comes to computer science algorithms, many people get confused: “I can understand, but I can’t use!” I can read it, but I can’t use it! It’s like visiting the murals in the Mogao Grottoes, seeing it, feeling it, but not being able to walk into it. We just need a key to open the door of the algorithm, just like tao Yuanming’s “Peach Blossom Land” in “the beginning is very narrow, only then universal. Ten more steps and suddenly it was clear.”
Today xiaobian brings a large list of algorithm books, I hope to take you to answer questions.
Classic introduction to artificial intelligence
Artificial Intelligence (2nd Edition)
By Stephen Lucci
American classic introductory textbook, known as the encyclopedia of artificial intelligence field. The most advanced course in the field of artificial intelligence in the last decade is more suitable for undergraduates.
Based on the theoretical foundation of ARTIFICIAL intelligence, this book presents readers with a comprehensive, novel, colorful and easy-to-understand body of artificial intelligence knowledge. Examples, applications, full-color images, and anecdotes are provided to stimulate reading and learning. Advanced courses in robotics and machine learning, including neural networks, genetic algorithms, natural language processing, planning and complex board games, have also been introduced.
Deep Learning
By Ian Goodfellow
Bi read books for all data scientists and machine learning practitioners! Recommended by tesla CEO Elon Musk and many other experts at home and abroad!
Deep learning is a branch of machine learning that enables computers to learn experiences and understand the world through hierarchical concepts. Because computers can derive knowledge from experience, there is no need for a human to formally define all the knowledge a computer needs. Hierarchical concepts allow computers to learn complex concepts by constructing simple concepts, and these hierarchical graph structures will have deep layers. This book will cover many topics in the field of deep learning.
Neural Network Programming in Python
[Britain] By Tariq Rashid
At present, the development and application of deep learning and artificial intelligence have left a deep impression on people.
In a lighthearted manner, this book reveals the mathematical ideas of neural networks step by step and shows how to develop neural networks using the Python programming language.
This book will take you on a fun yet methodical journey, starting with a very simple idea and gradually understanding how neural networks work. You don’t need to know any math beyond high school, and this book provides an easy-to-understand introduction to calculus.
You will learn to develop your own neural network in Python and train it to recognize handwritten numbers, even as well as professional neural networks.
This book is for those who want to learn about deep learning, artificial intelligence, and neural networks, and especially for those who want to develop neural networks through Python programming.
Fundamentals of Algorithms
Python Algorithms Tutorial
By Magnus Lie Hetland
Author of the bestselling Python Basics tutorial (2nd edition) Knowledge points clear, concise language.
This book uses Python language to explain the analysis and design of algorithms, focusing on classical algorithms, to help readers understand basic algorithm problems and solve problems to lay a good foundation.
This book uses Python to explain the analysis and design of algorithms. This book focuses on classical algorithms, but provides a good foundation for understanding basic algorithmic problems and solving them.
The concepts and knowledge points are clearly explained and the language is concise. This book is suitable for beginners and self-learners who are interested in Python algorithms. It is also suitable for computer science students in colleges and universities.
Algorithms of Fun
Xiao-yu Chen zhao
This book from the beauty of the algorithm, no profound principles, no boring formula, through the interesting story leads to algorithm problems, including
In combination with students’ questions, the essence of the algorithm is analyzed, and the detailed process and running results of the code implementation are given.
This book can be used as a learning book for programmers, but also suitable for beginners who have never had programming experience but have a strong interest in algorithms. It can also be used as a book for teachers and students of computer, mathematics and related majors in colleges and universities and teaching materials for training schools.
“Algorithm Learning and Application from Entry to Mastery”
Linda the
The feature of this book is to realize the integration of most of the contents of introductory knowledge, example demonstration, example drill, technical solution and comprehensive combat, so that readers can understand, use and learn. The volume of a book explains the contents of three types of books, such as category, example and project. Rich supporting resources, learning more efficient.
Crack the learning difficulties “technical explanation” → example exercise “→” Technical solution “throughout the book, a comprehensive grasp of the algorithm application technology explanation: through 320 examples, explained each knowledge point of algorithm application step by step. Example walkthrough: 5 comprehensive examples to equip readers with the ability to apply algorithms to real projects. Technical clarification: explain and analyze easily confused concepts separately to help readers bypass the pitfalls in learning.
Algorithmic Puzzles
By Anany Levitin
Algorithms are one of the most important building blocks in computer science. Algorithmic puzzles are those that can be solved directly or indirectly by using algorithms. Solving algorithmic puzzles is the most effective and fun way to cultivate and exercise algorithmic thinking ability.
. The book includes some of the oldest puzzles that math and computer science have learned from. There are also some relatively new puzzles in the book, some of which have been used as interview questions for well-known IT companies. The book is divided into four sections: overview, puzzles, tips and answers. The general strategy of algorithm design and the technique of algorithm analysis are introduced in this paper. The puzzles section lists the puzzles as easy, medium, and hard. In the hint section, puzzle hints are given in turn to help readers find the right direction to solve the problem, while still leaving room for readers to solve independently. The answers section gives detailed answers to the puzzles.
Programming the Way: Interviews and Algorithms
Out the
Transformed into CSDN technology blog “Structure method algorithm approach”, the content covers interview, algorithm, machine learning three themes; The author’s accumulated achievements in several years; Enter the IT industry job examination and interview treasure book
.From Chapter 1 to Chapter 6, programming questions and algorithms related to string, array, tree, search, dynamic programming, mass data processing and so on are explained respectively. Chapter 7 introduces two algorithms of machine learning — K-nearest Neighbor and SVM. Each question in the book is a frequent interview question, repeatedly appeared in the written test and interview of major companies in the past five years, has a strong reference value for interview preparation.
Machine learning algorithms
Python Machine Learning — Core Algorithms for Predictive Analysis
By Michael Bowles
When learning and researching machine learning, novice machine learning students are often at a loss when faced with a bewildering array of algorithms. This book
To help readers understand machine learning.
The book focuses on
And a code example is given to illustrate the principle of using the algorithm discussed. The book is divided into seven chapters, which discuss in detail two kinds of core algorithms of prediction model, construction of prediction model, application and implementation of penalty linear regression and integration method. This book is aimed at Python developers who want to improve their machine learning skills by helping them solve a particular project or improve related skills.
Python Machine Learning Practice Guide
Alexander T. Combs
Machine learning is an increasingly popular field in recent years, and Python has gradually become one of the mainstream programming languages after a period of development.
The book consists of 10 chapters. Chapter 1 covers the Python machine learning ecosystem. The remaining nine chapters cover a wide range of machine learning-related algorithms, including classification algorithms, data visualization techniques, recommendation engines, and more. These include applications of machine learning in apartments, airline tickets, IPO markets, news feeds, content promotion, stock markets, graphics, chatbots and recommendation engines. The book is for Python programmers, data analysts, readers interested in algorithms, practitioners in the field of machine learning, and researchers.
Neural Network Algorithm and Implementation — Based on Java Language
By Alan M.F. Souza (Allen)
Java language is one of the most suitable tools for implementing neural networks, and it is also one of the most popular programming languages at present. It contains a variety of APIS and packages for development, and has the portability of “write once, run anywhere”.
First, you will learn the basics of neural networks, perceptrons and their characteristics. You will then use the concepts learned to implement self-organizing mapping networks. In addition, you will learn about applications such as weather forecasting, disease diagnosis, customer profiling and optical character recognition (OCR). Finally, you will learn methods for real-time optimization and adaptive neural networks.
Anatomy of Classical Machine Learning Algorithms based on OpenCV
Zhao chunjian, a
This book is for the purpose, for normal bayesian classifier, K neighbor algorithm, support vector machines, decision tree, AdaBoost, extreme stochastic gradient increasing tree, random forests, trees, the maximum expected this 10 classic machine learning, neural network algorithm to the principle of concrete analysis, then give relevant OpenCV source sentence by sentence explain, Finally, an application example based on OpenCV is completed.
Image Local Feature Detection and Description
Zhao chunjian, a
This book uses OpenCV 2.4.9 as the research tool, All the latest feature detection and description algorithms implemented — K-R, Canny, Harris, Shi-Tomasi, FAST, MSER, MSCR, SIFT, SURF, BRISK, BRIEF, ORB, FREAK, CenSurE, etc. This paper not only analyzes their principles and implementation methods, but also analyzes the source code in detail, and gives a specific example of program implementation, which fully embodies the characteristics of combining theory with practice.
Algorithms on Text: Natural Language Processing in Its Simplest form
Pieter van rooyen male with
, explains natural language processing and machine learning techniques in simple and profound ways, and has been read over 300,000 times on weibo.
This book combines the author’s years of learning and working experience in natural language processing, and tries to introduce the theory, methods and techniques of natural language processing in a vivid and simple way.
The first chapters of this book introduce the mathematical basics necessary for learning machine learning to help readers quickly acquire the knowledge and skills necessary for natural language processing. This book is suitable for readers who are engaged in the research and work related to natural language processing, especially for those who want to understand and master machine learning or natural language processing technologies.
Face Recognition Principle and Algorithm — Research on Dynamic Face Recognition System
Xiong Zhiyong, Shen Li, Liu Yiguang
This paper summarizes the research achievements of face recognition algorithm in recent years, and provides specific algorithm implementation and research results, which can provide a good reference for researchers in this field.
Through reading this book, readers can systematically learn the methods of face recognition research, understand the realization of specific algorithms of face recognition research and the latest progress of relevant technologies at home and abroad. The dynamic face recognition method is an attempt and extension of the author in face recognition research, and it is hoped that this part can provide a new research branch in this field.
Bayesian analysis
Python Bayesian Analysis
[Argentina] By Osvaldo Martin
PyMOL community activists dedicated! Discover the power of Python Bayesian analysis!
This book introduces the main concepts in Bayesian statistics and the methods for applying them to data analysis. All bayesian models in this book are implemented using PyMC3. PyMC3 is a Python library for probabilistic programming, and many of its features are described in the book. With the help of this book and PyMC3, readers will learn to implement, examine, and extend Bayesian statistical models to solve a range of data analysis problems.
Bayesian Thinking: A Python Approach to Statistical Modeling
By Allen B. Downey
This book helps people who want to use mathematical tools to solve practical problems,
Bayes method is a common mathematical method to solve uncertain problems by using probability knowledge. For a computer professional, he should be familiar with its application in the field of computer problems such as machine translation, speech recognition, spam detection and so on.
Probability Programming In Action
Avi Pfeffer by Avi Pfeffer
Introduction by Stuart Russell, a pioneer in artificial intelligence and professor at the University of California, Berkeley. An incredible Scala probability programming practice book!
Probabilistic reasoning is one of the core methods of machine learning, and this book aims to demystitize probabilistic modeling for programmers, especially Scala developers, to help them efficiently use probabilistic programming systems.
With a probabilistic programming system, your program can determine the probabilities of different conclusions by applying specific algorithms. This means you can predict future events such as sales trends, computer system failures, test results, and many other important concerns.
Deep Learning
Deep Learning and TensorFlow In Action
Li Jianjun, Wang Ximing, Pan Mian, Xu Shuogui, Kong Dexing, Zhang Zhenzhen, Xu Guoqing
Firstly, the development history of neural network is briefly introduced, and TensorFlow is introduced. The working mechanism of TensorFlow is demonstrated in the book with a simple unary linear regression housing price prediction model. Third, several open source projects based on TensorFlow are briefly cited. This paper introduces the denotation of deep neural network: machine learning. The book focuses on the three elements of machine learning: Task, Performance and Experience, and expounds the principle of machine learning model building.
Principles and Practices of Deep Learning
By Chen Zhongming and Peng Lingxi
This book systematically and comprehensively introduces all aspects of deep learning knowledge step by step, including technical experience, use skills and practical cases. This book introduces in detail the common network models related to deep learning at present, as well as the algorithm principles and core ideas of different network models. This book uses a large number of examples to analyze the network model, these cases can deepen readers’ understanding of the network model.
In addition, the book also provides complete advanced content and corresponding cases, so that readers can have a comprehensive and in-depth understanding of deep learning knowledge and skills, to achieve the purpose of learning to apply.
Python Deep Learning
[Britain] By N.D. Lewis
This book is a beginner’s guide to deep learning practices using Python. This book does not list a large number of formulas, but introduces the two tasks of deep neural network — classification and regression in a simple and straightforward way through some practical practical cases, and analyzes some core problems in the deep learning model, in order to give readers a clear understanding of the overall picture of deep learning.
Keras Deep Learning Practice
[Italy] Antonio Gulli
This book uses the current popular Keras framework to implement a large number of deep learning algorithms, build a number of deep learning models, and introduces the application of deep learning in games and other practical occasions, especially the book also introduces the current hot generative adversarial network (GAN) application. The book is easy to understand and emphasizes practical cases, which is suitable for practitioners and enthusiasts of machine learning to get started and practice.
Mastering Data Science: From Linear Regression to Deep Learning
Gen tang zhao
Introduction to the field of data science, introduces the common tools of data science — Python, mathematical foundations and models, and discusses the frontier fields of data science — big data and artificial intelligence, including classical models in the field of machine learning, distributed machine learning, neural networks and deep learning, etc.
Python classic article
Python Programming (Version 3)
By John Zelle
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This book teaches computer programming using Python as a tool. The book emphasizes problem solving, design, and programming as core skills in computer science. The book is easy to read and learn, with its distinctive features and interesting examples. It is suitable for beginners of Python, as well as for college computer science teachers and students.
Python Language Description of Data Structures
By Kenneth A. Lambert
In computer science, data structure is an advanced course with abstract concepts and great difficulty. The Syntax of Python is simple and interactive. Using Python to explain topics like data structures is easier and clearer than, say, C.
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