Programming Computer Science

From Oxford's leading AI researcher comes a fun and accessible tour through the history and future of one of the most cutting edge and misunderstood field in science: Artificial Intelligence The somewhat ill-defined long-term aim of AI is to build machines that are conscious, self-aware, and...

Inside, you will find an introduction to JavaScript, HTML, CSS, and SQL. These are computer programming languages. Some of them are more precisely referred to as scripting languages. Starting with JavaScript, I will introduce you to the above-mentioned programming languages. Hopefully, by the end of this audiobook, you will have the answers to the questions of what it is and what you can do with it.

Learning the Python programming language is not difficult; it is one of the easiest of all the programming languages to learn. By working through my in-depth yet straightforward guide, you, too, will master Python and start putting your knowledge to good use.

This book is for engineering leaders and experienced developers who want to build serious software with AI—and actually unlock the leverage it offers. It introduces a clear, step-by-step workflow that makes this approach reliable, repeatable, and scalable. You’ll learn a practical, disciplined approach to building applications—not with AI as a co-pilot, but as the primary developer.

Have you ever wanted to create your own computer software, app, or website? In this day and age where technology is integrated into the foundation of our lives, have you ever thought to yourself, “How do I create the next WhatsApp?” or “How do I create the next Facebook?”

Go in Action is for any intermediate-level developer who has experience with other programming languages and wants a jump-start in learning Go or a more thorough understanding of the language and its internals. This book provides an intensive, comprehensive, and idiomatic view of Go. It focuses on the specification and implementation of the language, including topics like language syntax, Go's type system, concurrency, channels, and testing.

Easy to understand and engaging, this updated edition of Introducing Python is ideal for beginning programmers as well as those new to the language. Author Bill Lubanovic takes you from the basics to more involved and varied topics, mixing tutorials with cookbook-style code recipes to explain concepts in Python 3. End-of-chapter exercises help you practice what you've learned.

This friendly guide teaches you how to apply common algorithms to the practical problems you face every day as a programmer. You'll start with sorting and searching and, as you build up your skills in thinking algorithmically, you'll tackle more complex concerns such as data compression and artificial intelligence. This accesible introduction is suitable for self-taught programmers, engineers, or anyone who wants to brush up on algorithms. Each carefully presented example includes helpful diagrams and fully annotated code samples in Python.

In Once Upon an Algorithm, Martin Erwig explains computation as something that takes place beyond electronic computers, and computer science as the study of systematic problem solving. Erwig points out that many daily activities involve problem solving. Getting up in the morning, for example: You get up, take a shower, get dressed, eat breakfast. This simple daily routine solves a recurring problem through a series of well-defined steps. In computer science, such a routine is called an algorithm.

Have you ever wondered how computers follow instructions so well? Or how they do math so quickly? Basic principles of coding, including variables, binary code, loops, programming languages, and more, are explored in this newest engaging addition to the How Do series.

Get advice on how to leverage AI development tools for all stages of code creation, including requirements, planning, design, coding, debugging, testing, and documentation. With this book, beginners and experienced developers alike will learn how to use a wide range of tools, from general-purpose LLMs to code-specific systems. You'll also learn about more specialized generative AI tools for tasks such as text-to-image creation. Tom Taulli provides a methodology for modular programming that aligns effectively with the way prompts create AI-generated code.

Natural Language Processing in Action is your guide to building machines that can read and interpret human language. In it, you'll use readily available Python packages to capture the meaning in text and react accordingly. The book expands traditional NLP approaches to include neural networks, modern deep learning algorithms, and generative techniques as you tackle real-world problems like extracting dates and names, composing text, and answering free-form questions.

The Secret Science of Games is an accessible and entertaining guide to the hidden role of science in making games more fun. User experience (UX) research is a vital part of the story of how video games have become a cultural juggernaut, but game developers generally don’t speak publicly about how data shapes game design and game development.

Artificial intelligence is transforming lives and revolutionizing industries. This accessible guide to generative AI and machine learning is for tech enthusiasts and forward-thinkers alike—whether you're in IT or a professional from another field looking to stay ahead and curious about AI’s impact on the future, this book provides the essential insights you need to stay competitive and informed in the Age of AI.

Big Data teaches you to build big data systems using an architecture designed specifically to capture and analyze web-scale data. This book presents the Lambda Architecture, a scalable, easy-to-understand approach that can be built and run by a small team. You'll explore the theory of big data systems and how to implement them in practice. In addition to discovering a general framework for processing big data, you'll learn specific technologies like Hadoop, Storm, and NoSQL databases.

Real-World Machine Learning will teach you the concepts and techniques you need to be a successful machine learning practitioner without overdosing you on abstract theory and complex mathematics. By working through immediately relevant examples in Python, you'll build skills in data acquisition and modeling, classification, and regression. You'll also explore the most important tasks like model validation, optimization, scalability, and real-time streaming. When you're done, you'll be ready to successfully build, deploy, and maintain your own powerful ML systems.

If you want to learn how to program in Python, but don't know where to start read on. Knowing where to start when learning a new skill can be a challenge, especially when the topic seems so vast. There can be so much information available that you can't even decide where to start. Or worse, you start down the path of learning and quickly discover too many concepts, commands, and nuances that aren't explained.

Author Subrata Dasgupta named the two decades from 1970 to 1990 as the second age of computer science to distinguish it from the preceding genesis of the science and the age of the Internet/World Wide Web that followed. This book describes the evolution of computer science in this second age in the form of seven overlapping, intermingling, parallel histories that unfold concurrently in the course of the two decades.

A few decades into the digital era, scientists discovered that thinking in terms of computation made possible an entirely new way of organizing scientific investigation; eventually, every field had a computational branch: computational physics, computational biology, computational sociology. More recently, "computational thinking" has become part of the K-12 curriculum. But what is computational thinking? This volume in the MIT Press Essential Knowledge series offers an accessible overview.

Would you like to learn to use Python extracting meaningful insight from data to grow your business but you reckon it will be too complex? As a beginner, you will be probably afraid that programming is difficult. Learning data analysis and data mining can take months, and the possibility to give up before mastering them could be high. The best solution is a complete programming manual with hands-on projects and practical exercises. This guide is structured as a course with seven chapters for seven days and studied special exercises to apply what you learned step-by-step.