Algorithmic Thinking: Learning Algorithms To Level Up Your Coding Skills

Zingaro, Daniel

No Starch Press

Learn AI-Assisted Python Programming, Second Edition: With GitHub Copilot and ChatGPT

Computers & Technology

Porter, Leo

Computer Science

Manning

Writing computer programs in Python just got a lot easier! Use AI-assisted coding tools like GitHub Copilot and ChatGPT to turn your ideas into applications faster than ever.\nAI has changed the way we write computer programs. With tools like Copilot and ChatGPT, you can describe what you want in plain English, and watch your AI assistant generate the code right before your eyes. It’s perfect for beginners, or anyone who’s struggled with the steep learning curve of traditional programming.\nIn Learn AI-Assisted Python Programming: With GitHub Copilot and ChatGPT you’ll learn how to:\nWrite fun and useful Python applications—no programming experience required! Use the Copilot AI coding assistant to create Python programs Write prompts that tell Copilot exactly what to do Read Python code and understand what it does Test your programs to make sure they work the way you want them to Fix code with prompt engineering or human tweaks Apply Python creatively to help out on the job 
Learn AI-Assisted Python Programming: With GitHub Copilot and ChatGPT is a hands-on beginner’s guide that is written by two esteemed computer science university professors. It teaches you everything you need to start programming Python in an AI-first world. You’ll hit the ground running, writing prompts that tell your AI-assistant exactly what you want your programs to do. Along the way, you’ll pick up the essentials of Python programming and practice the higher-level thinking you’ll need to create working apps for data analysis, automating tedious tasks, and even video games.\nForeword by Beth Simon, Ph.D.\nPurchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.\nAbout the technology\nThe way people write computer programs has changed forever. Using GitHub Copilot, you describe in plain English what you want your program to do, and the AI generates it instantly.\nAbout the book\nThis book shows you how to create and improve Python programs using AI—even if you’ve never written a line of computer code before. Spend less time on the slow, low-level programming details and instead learn how an AI assistant can bring your ideas to life immediately. As you go, you’ll even learn enough of the Python language to understand and improve what your AI assistant creates.\nWhat's inside\nPrompts for working code Tweak code manually and with AI help AI-test your programs Let AI handle tedious details 
About the reader\nIf you can move files around on your computer and install new programs, you can learn to write useful software!\nAbout the author\nDr. Leo Porter is a Teaching Professor at UC San Diego. Dr. Daniel Zingaro is an Associate Teaching Professor at the University of Toronto. The technical editor on this book was Peter Morgan.\nTable of Contents\n1 Introducing AI-assisted programming with Copilot 
2 Getting started with Copilot 
3 Designing functions 
4 Reading Python code – Part 1 
5 Reading Python Code – Part 2 
6 Testing and prompt engineering 
7 Problem decomposition 
8 Debugging and better understanding your code 
9 Automating tedious tasks 
10 Making some games 
11 Future directions

Learn AI-assisted Python Programming: With GitHub Copilot and ChatGPT

Readers tackle challenging topics like recursion, dynamic programming, graphs, greedy algorithms, heaps, hash tables, segment trees, and other data structures for efficiently handling data. The book contains no pseudocode: all code is written in C and is thoroughly explained in the text (C is a de facto programming language for programming competitions). Zingaro also shows how several problems can be reduced to algorithms on graphs.

Algorithmic Thinking: A Problem-Based Introduction

Programming

Learn to Program by Solving Problems is a practical introduction to programming using Python, one of the world's most popular programming languages. The book emphasises problem-solving strategies that teach readers not only the mechanics of coding, but how to think like savvy programmers. Teaches readers how to use Python to solve short, situational problems (for example, how to predict when a gambler will run out of money while playing slot machines; how to create a programme to track cell phone data usage; how to set up a system of identifying the popularity of berths in a parking lot).

Learn to Code by Solving Problems: A Python Programming Primer

Get in the game and learn essential computer algorithms by solving competitive programming problems, in the fully revised second edition of the bestselling original. (Still no math required!) Knowing how to design algorithms will take you from being a good programmer to a great programmer. This completely revised second edition teaches you how to design your own rocket-fast, right-for-the-task algorithms--minus the proofs and complex math. Forget the useless pseudocode and played-out examples you've seen in other books. Author and award-winning educator Dan Zingaro draws problems straight from online programming competitions to rigorously teach you all of the heavyweights you need to know, like hash tables, recursion, trees, graphs, and heaps. As he guides you to the perfect algorithmic solution for each unique programming puzzle, you'll build up a toolkit of go-to algorithms for quickly and correctly solving any problem you come across. The second edition features several entirely new chapters on dynamic programming and randomized algorithms, as well as more effective problems and enhanced explanations. Code examples are provided using the C language. Learn how to: <ul> <li>Classify problems, choose data structures, and identify appropriate algorithms </li> <li>Choose between data structures like hash tables, heaps, or trees, based on how they affect runtime and speed </li> <li>Adopt powerful strategies like recursion, dynamic programming, and binary search to solve challenging problems </li> <li>Apply the breadth-first search algorithm to find the optimal way to play a board game, Dijkstra's algorithm to determine the fastest routes between two locations, and many more </li> </ul>

Algorithmic Thinking, 2nd Edition: Learn Algorithms to Level Up Your Coding Skills

Algorithms are central to all areas of computer science, from compiler construction to numerical analysis to artificial intelligence. Throughout your academic and professional careers, you may be required to construct new algorithms, analyze existing algorithms, or modify algorithms to suit new purposes. How do we know that such algorithms are correct? One method involves making claims about how we expect our programs to operate, and then constructing code that carries out these tasks. The key component of such reasoning is the invariant, and is the topic of this book. In these pages, you will study how invariants are developed, how they are used to construct correct algorithms, and how they are helpful in analyzing existing programs. Along the way, you'll be introduced to some classic sorting, searching and mathematical algorithms, and even some solutions to games and logic puzzles. These examples, though, are only conduits for the loftier goal: understanding why algorithms work.

Invariants: A Generative Approach to Programming (Texts in Computer Science)

College Publications

Python bez problem: reshaem realnye zadachi i pishem poleznyy kod

Piter Press

Start Competitive Programming!: Ace the USACO Bronze Competition

Baharav, Zachi

Independently published

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