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How To Code in Go
How To Code in Go

This book is designed to introduce you to writing programs with the Go programming language. You'll learn how to write useful tools and applications that can run on remote servers, or local Windows, macOS, and Linux systems for development. The topics that it covers include how to: - Install and set up a local Go development environment on Windows, macOS, and Linux systems; - Design your programs with conditional logic, including switch statements to control program flow; - Define your own data structures and create interfaces to them for reusable code; - Write custom error handling functions; - Building and installing your Go programs so that they can run on different operating systems and different CPU architectures; - Using flags to pass arguments to your programs, to override default options. Each chapter can be read on its own or used as a reference, or you can follow the chapters from beginning to end. Feel free to jump to the chapter or chapters that best suits your ...
Reinforcement Learning
Reinforcement Learning

Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI professionals how to learn by reinforcement and enable a machine to learn by itself. Author Phil Winder of Winder Research covers everything from basic building blocks to state-of-the-art practices. You'll explore the current state of RL, focus on industrial applications, learn numerous algorithms, and benefit from dedicated chapters on deploying RL solutions to production. This is no cookbook; doesn't shy away from math and expects familiarity with ML. Learn what RL is and how the algorithms help solve problems; Become grounded in RL fundamentals including Markov decision processes, dynamic programming, and temporal difference learning; Dive deep into a ran ...
Algorithmic Thinking
Algorithmic Thinking

Algorithmic Thinking will teach you how to solve challenging programming problems and design your own algorithms. Daniel Zingaro, a master teacher, draws his examples from world-class programming competitions like USACO and IOI. You'll learn how to classify problems, choose data structures, and identify appropriate algorithms. You'll also learn how your choice of data structure, whether a hash table, heap, or tree, can affect runtime and speed up your algorithms; and how to adopt powerful strategies like recursion, dynamic programming, and binary search to solve challenging problems. Line-by-line breakdowns of the code will teach you how to use algorithms and data structures like: - The breadth-first search algorithm to find the optimal way to play a board game or find the best way to translate a book; - Dijkstra's algorithm to determine how many mice can exit a maze or the number of fastest routes between two locations; - The union-find data structure to answer questions about c ...
Racket Programming the Fun Way
Racket Programming the Fun Way

At last, a lively guided tour through all the features, functions, and applications of the Racket programming language. You'll learn a variety of coding paradigms, including iterative, object oriented, and logic programming; create interactive graphics, draw diagrams, and solve puzzles as you explore Racket through fun computer science topics - from statistical analysis to search algorithms, the Turing machine, and more. Early chapters cover basic Racket concepts like data types, syntax, variables, strings, and formatted output. You'll learn how to perform math in Racket's rich numerical environment, and use programming constructs in different problem domains (like coding solutions to the Tower of Hanoi puzzle). Later, you'll play with plotting, grapple with graphics, and visualize data. Then, you'll escape the confines of the command line to produce animations, interactive games, and a card trick program that'll dazzle your friends. You'll learn how tot: - Use DrRacket, an inte ...
Learning TensorFlow.js
Learning TensorFlow.js

Given the demand for AI and the ubiquity of JavaScript, TensorFlow.js was inevitable. With this Google framework, seasoned AI veterans and web developers alike can help propel the future of AI-driven websites. In this guide, author Gant Laborde (Google Developer Expert in machine learningand the web) provides a hands-on end-to-end approach to TensorFlow.js fundamentals for a broad technical audience that includes data scientists, engineers, web developers, students, and researchers. You'll begin by working through some basic examples in TensorFlow.js before diving deeper into neural network architectures, DataFrames, TensorFlow Hub, model conversion, transfer learning, and more. Once you finish this book, you'll know how to build and deploy production-readydeep learning systems with TensorFlow.js. - Explore tensors, the most fundamental structure of machine learning; - Convert data into tensors and back with a real-world example; - Combine AI with the web using TensorFlow.js; ...
Mastering Python for Bioinformatics
Mastering Python for Bioinformatics

Life scientists today urgently need training in bioinformatics skills. Too many bioinformatics programs are poorly written and barely maintained, usually by students and researchers who've never learned basic programming skills. This practical guide shows postdoc bioinformatics professionals and students how to exploit the best parts of Python to solve problems in biology while creating documented, tested, reproducible software. Ken Youens-Clark, author of Tiny Python Projects (Manning), demonstrates not only how to write effective Python code but also how to use tests to write and refactor scientific programs. You'll learn the latest Python features and tools including linters, formatters, type checkers, and tests to create documented and tested programs. You'll also tackle 14 challenges in Rosalind, a problem-solving platform for learning bioinformatics and programming. - Create command-line Python programs to document and validate parameters - Write tests to verify refactor p ...
Beginning T-SQL, 4th Edition
Beginning T-SQL, 4th Edition

Get a performance-oriented introduction to the T-SQL language underlying the Microsoft SQL Server and Azure SQL database engines. This fourth edition is updated to include SQL Notebooks as well as up-to-date syntax and features for T-SQL on-premises and in the Azure cloud. Exercises and examples now include the WideWorldImporters database, the newest sample database from Microsoft for SQL Server. Also new in this edition is coverage of JSON from T-SQL, news about performance enhancements called Intelligent Query Processing, and an appendix on running SQL Server in a container on macOS or Linux. Beginning T-SQL starts you on the path to mastering T-SQL with an emphasis on best practices. Using the sound coding techniques taught in this book will lead to excellent performance in the queries that you write in your daily work. Important techniques such as windowing functions are covered to help you write fast-executing queries that solve real business problems.The book begins with an i ...
Practical Machine Learning for Computer Vision
Practical Machine Learning for Computer Vision

This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras. You'll learn how to: Design ML architecture for computer vision tasks; Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropri ...
SQL Pocket Guide, 4th Edition
SQL Pocket Guide, 4th Edition

If you use SQL in your day-to-day work as a data analyst, data scientist, or data engineer, this popular pocket guide is your ideal on-the-job reference. You'll find many examples that address the language's complexities, along with key aspects of SQL used in Microsoft SQL Server, MySQL, Oracle Database, PostgreSQL, and SQLite. In this updated edition, author Alice Zhao describes how these database management systems implement SQL syntax for both querying and making changes to a database. You'll find details on data types and conversions, regular expression syntax, window functions, pivoting and unpivoting, and more. Quickly look up how to perform specific tasks using SQL; Apply the book's syntax examples to your own queries; Update SQL queries to work in five different database management systems; Connect Python and R to a relational database; Look up frequently asked SQL questions in the "How Do I?" chapter. ...
Financial Theory with Python
Financial Theory with Python

Nowadays, finance, mathematics, and programming are intrinsically linked. This book provides the relevant foundations of each discipline to give you the major tools you need to get started in the world of computational finance. Using an approach where mathematical concepts provide the common background against which financial ideas and programming techniques are learned, this practical guide teaches you the basics of financial economics. Written by the best-selling author of Python for Finance, Yves Hilpisch, Financial Theory with Python explains financial, mathematical, and Python programming concepts in an integrative manner so that the interdisciplinary concepts reinforce each other. Draw upon mathematics to learn the foundations of financial theory and Python programming; Learn about financial theory, financial data modeling, and the use of Python for computational finance; Leverage simple economic models to better understand basic notions of finance and Python programming co ...
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