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Before there were computers, there were algorithms. But now that there are computers, there are even more algorithms, and algorithms lie at the heart of computing. This book provides a comprehensive introduction to the modern study of computer algorithms. It presents many algorithms and covers them in considerable depth, yet makes their design and analysis accessible to all levels of readers. We have tried to keep explanations elementary without sacrificing depth of coverage or mathematical rigor. Each chapter presents an algorithm, a design technique, an application area, or a related topic. Algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The book contains 244 figures—many with multiple parts—illustrating how the algorithms work. Since we emphasize efficiency as a design criterion, we include careful analyses of the running times of all our algorithms. The text is intended primarily for use in undergraduate or graduate courses in algorithms or data structures. Because it discusses engineering issues in algorithm design, as well as mathematical aspects, it is equally well suited for self-study by technical professionals. In this, the third edition, we have once again updated the entire book. The changes cover a broad spectrum, including new chapters, revised pseudocode, and a more active writing style.

Unix is distinguished by a simple, coherent, and elegant design — truly remarkable features that have enabled the system to influence the world for more than a quarter of a century. And especially thanks to the growing presence of Linux, the idea is still picking up momentum, with no end of the growth in sight. Unix and Linux carry a certain fascination, and the two quotations above hopefully capture the spirit of this attraction. Consider Dennis Ritchie’s quote: Is the coinventor of Unix at Bell Labs completely right in saying that only a genius can appreciate the simplicity of Unix? Luckily not, because he puts himself into perspective immediately by adding that programmers also qualify to value the essence of Unix. Understanding the meagerly documented, demanding, and complex sources of Unix as well as of Linux is not always an easy task. But once one has started to experience the rich insights that can be gained from the kernel sources, it is hard to escape the fascination of Linux. It seems fair to warn you that it’s easy to get addicted to the joy of the operating system kernel once starting to dive into it. This was already noted by Benny Goodheart and James Cox, whose preface to their book The Magic Garden Explained (second quotation above) explained the internals of Unix System V. And Linux is definitely also capable of helping you to lose your mind!

I'm glad you're here. It's about time we talked about machine learning. Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. As a subfield of data science, machine learning enables computers to learn through experience: to make predictions about the future using collected data from the past. And the amount of data to be analyzed is enormous! Current estimates put the daily amount of produced data at 2.5 exabytes (or roughly 1 billion gigabytes). Can you believe it? This would be enough data to fill up 10 million blu-ray discs, or amount to 90 years of HD video. In order to deal with this vast amount of data, companies such as Google, Amazon, Microsoft, and Facebook have been heavily investing in the development of data science platforms that allow us to benefit from machine learning wherever we go—scaling from your mobile phone application all the way to supercomputers connected through the cloud.

Every programmer has a story about how they learned to write their first program. I started learning as a child when my father was working for Digital Equipment Corporation, one of the pioneering companies of the modern computing era. I wrote my first program on a kit computer my dad had assembled in our basement. The computer consisted of nothing more than a bare motherboard connected to a keyboard without a case, and it had a bare cathode ray tube for a monitor. My initial program was a simple number guessing game, which looked something like this

“You’ve just done in two hours what it takes the three of us two days to do.” My college roommate was working at a retail electronics store in the early 2000s. Occasionally, the store would receive a spreadsheet of thousands of product prices from its competitor. A team of three employees would print the spreadsheet onto a thick stack of paper and split it among themselves. For each product price, they would look up their store’s price and note all the products that their competitors sold for less. It usually took a couple of days.

Mastering ROS for Robotics Programming is an advanced guide of ROS that is very suitable for readers who already have a basic knowledge in ROS. ROS is widely used in robotics companies, universities, and robotics research institutes for designing, building, and simulating a robot model and interfacing it into real hardware. ROS is now an essential requirement for Robotic engineers; this guide can help you acquire knowledge of ROS and can also help you polish your skills in ROS using interactive examples. Even though it is an advanced guide, you can see the basics of ROS in the first chapter to refresh the concepts. It also helps ROS beginners. The book mainly focuses on the advanced concepts of ROS, such as ROS Navigation stack, ROS MoveIt!, ROS plugins, nodelets, controllers, ROS Industrial, and so on.

Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge. The goal of R for Data Science is to help you learn the most important tools in R that will allow you to do data science. After reading this book, you’ll have the tools to tackle a wide variety of data science challenges, using the best parts of R.