Classic Computer Science Problems in Python sharpens your CS problem-solving skills with time-tested scenarios, exercises, and algorithms, using Python. You'll tackle dozens of coding challenges, ranging from simple tasks like binary search algorithms to clustering data using k-means. You'll especially enjoy the feeling of satisfaction as you crack problems that connect computer science to the real-world concerns of apps, data, performance, and even nailing your next job interview!
Inside: Search algorithms Common techniques for graphs Neural networks Genetic algorithms Adversarial search Uses type hints throughout Covers Python 3. A fun way to get hands-on experience with classical computer science problems in modern Python. Jens Christian Bredahl Madsen, IT Relation Highly recommended to everyone who is interested in deepening their understanding, not only of the Python language, but also of practical computer science. Daniel Kenney-Jung, MD, University of Minnesota Classic problems presented in a wonderfully entertaining way with a language that always seems to have something new to offer.
Sharpen your coding skills by exploring established computer science problems! Classic Computer Science Problems in Java challenges you with time-tested scenarios and algorithms. Summary Sharpen your coding skills by exploring established computer science problems! This book collects the most useful solutions devised, guiding you through a variety of challenges and tried-and-true problem-solving techniques.
The principles and algorithms presented here are guaranteed to save you countless hours in project after project. About the book Classic Computer Science Problems in Java is a master class in computer programming designed around 55 exercises that have been used in computer science classrooms for years.
What's inside Recursion, memoization, and bit manipulation Search, graph, and genetic algorithms Constraint-satisfaction problems K-means clustering, neural networks, and adversarial search About the reader For intermediate Java programmers.
Table of Contents 1 Small problems 2 Search problems 3 Constraint-satisfaction problems 4 Graph problems 5 Genetic algorithms 6 K-means clustering 7 Fairly simple neural networks 8 Adversarial search 9 Miscellaneous problems 10 Interview with Brian Goetz. Dann geht es in die Praxis: Sie programmieren Ihr eigenes Neuronales Netz mit Python und bringen ihm bei, handgeschriebene Zahlen zu erkennen, bis es eine Performance wie ein professionell entwickeltes Netz erreicht.
Increase the performance of various neural network architectures using NEAT, HyperNEAT, ES-HyperNEAT, Novelty Search, SAFE, and deep neuroevolution Key Features Implement neuroevolution algorithms to improve the performance of neural network architectures Understand evolutionary algorithms and neuroevolution methods with real-world examples Learn essential neuroevolution concepts and how they are used in domains including games, robotics, and simulations Book Description Neuroevolution is a form of artificial intelligence learning that uses evolutionary algorithms to simplify the process of solving complex tasks in domains such as games, robotics, and the simulation of natural processes.
This book will give you comprehensive insights into essential neuroevolution concepts and equip you with the skills you need to apply neuroevolution-based algorithms to solve practical, real-world problems. You'll start with learning the key neuroevolution concepts and methods by writing code with Python.
You'll also get hands-on experience with popular Python libraries and cover examples of classical reinforcement learning, path planning for autonomous agents, and developing agents to autonomously play Atari games. Next, you'll learn to solve common and not-so-common challenges in natural computing using neuroevolution-based algorithms. Later, you'll understand how to apply neuroevolution strategies to existing neural network designs to improve training and inference performance.
Finally, you'll gain clear insights into the topology of neural networks and how neuroevolution allows you to develop complex networks, starting with simple ones. By the end of this book, you will not only have explored existing neuroevolution-based algorithms, but also have the skills you need to apply them in your research and work assignments.
What you will learn Discover the most popular neuroevolution algorithms — NEAT, HyperNEAT, and ES-HyperNEAT Explore how to implement neuroevolution-based algorithms in Python Get up to speed with advanced visualization tools to examine evolved neural network graphs Understand how to examine the results of experiments and analyze algorithm performance Delve into neuroevolution techniques to improve the performance of existing methods Apply deep neuroevolution to develop agents for playing Atari games Who this book is for This book is for machine learning practitioners, deep learning researchers, and AI enthusiasts who are looking to implement neuroevolution algorithms from scratch.
Working knowledge of the Python programming language and basic knowledge of deep learning and neural networks are mandatory. Programming Concepts in Python is one in a series of books that introduce the basic concepts of computer programming, using a selected programming language. The presentation of the material is the same in each language, and much of the text is identical. Code samples are specific to the selected language, and some unique language features are unavoidably included, but the presentation is largely language-independent.
A unique feature of the book is that it explains how to acquire, install, and use freely available software to edit, compile, and run console programs on just about any system, including Windows and Mac. Its examples use command line compiling, so that the presentation remains focused on programming concepts and avoids becoming a training tool for a specific IDE.
The three-part organization of material starts with the basics of sequential processing, then adds branching and looping logic and subprograms, and ends with arrays and objects. It turns a beginner with no programming experience into a programmer, prepared to continue their training in Python or just about any other specific programming language.
This student-friendly textbook encourages the development of programming skills through active practice by focusing on exercises that support hands-on learning. The Python Workbook provides a compendium of exercises, spanning a variety of academic disciplines and everyday situations. Solutions to selected exercises are also provided, supported by brief annotations that explain the technique used to solve the problem, or highlight a specific point of Python syntax. As you read the book, each code listing contains a file name that corresponds to a file in this repository.
The source code in this repository requires Python 3. Due to its extensive use of Python 3. You can find general questions and descriptive information about the book on the Classic Computer Science Problems website. Also, feel free to reach out to me on Twitter, davekopec. If you think you found an error in the source code, please open an issue up here on GitHub.
All of the source code in this repository is released under the Apache License version 2. Skip to content. For example, if you have some basic knowledge of Swift and a strong programming background, you will better understand protocol-oriented programming and Swift enums after reading Classic Computer Science Problems in Swift. These books are not data structures and algorithms textbooks. They don't claim to be that, and they are geared for a slightly different audience.
These are broad survey books that cover a large variety of computer science topics in a succinct, friendly, tutorial-like fashion, heavy with a lot of real code.
In other words, they are not meant to be academically rigorous, but instead fun while still being educational. Frankly, some of the cliche topics, like sorting, are left out because they will bore many of the self-taught programmers who will pickup the books.
The breadth of topics covered in the series actually stretches beyond the gamut of most data structures and algorithms textbooks by covering several topics from artificial intelligence.
About half of each book's content can be considered AI. On the other hand, yes, several traditional data structures and algorithms topics are left out. It's not likely to happen in the near-term. I think three programming languages is enough for a while.
We did three languages in three years Swift , Python , Java In the future, depending on the success of the Java book and the publisher's interest, I am open to working with a co-author on a "port" of the book to a language that I am less familiar with.
Go is one language the publisher and I have discussed. However, beyond Java, these books would be looking at release dates of or later if they happen. If you look at the Code section above though, you can find ports of the books' code to many other programming languages. If you are interested in acquiring the rights to translate one of the books into another language, please contact me and I will put you in touch with the appropriate person at the publisher.
However, you can often find them cheaper than that from third-party booksellers. Every English print edition copy comes with a way to download the eBook for free from Manning's website. Please let me know if the promo code does not work for you. Please let me know about it if you think it's just esoteric enough that I haven't heard of it, but it's still fairly classic.
I was fortunate to see most of the classic computer science problems in the books during my education at Dartmouth, so I must extend a thank you to the faculty there, especially Tom Cormen and Devin Balkcom. Since, once again see the earlier question on why certain data structure and algorithms topics are not in the series , these books are not textbooks, if you want a more academically rigorous introduction to the topics covered, I recommend the above mentioned textbooks.
I have worked professionally as a software developer and I am an active contributor to open source projects. I am also the author of Dart for Absolute Beginners Apress, but I no longer recommend that book since it is now significantly out-of-date.
I hold a bachelor's degree in economics from Dartmouth College and a master's degree in computer science, also from Dartmouth.
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