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Python Algorithms
Python Algorithms explains the Python approach to algorithm analysis and design. Written by Magnus Lie Hetland, author of Beginning Python, this book is sharply focused on classical algorithms, but it also gives a solid understanding of fundamental algorithmic problem-solving techniques. * The book deals with some of the most important and challenging areas of programming and computer science, but in a highly pedagogic and readable manner. * The book covers both algorithmic theory and programming practice, demonstrating how theory is reflected in real Python programs. * Well-known algorithms and data structures that are built into the Python language are explained, and the user is shown how to implement and evaluate others himself. What you'll learn * Transform new problems to well-known algorithmic problems with efficient solutions, or show that the problems belong to classes of problems thought not to be efficiently solvable. * Analyze algorithms and Python programs both using mathematical tools and basic experiments and benchmarks. * Prove correctness, optimality, or bounds on approximation error for Python programs and their underlying algorithms. * Understand several classical algorithms and data structures in depth, and be able to implement these efficiently in Python. * Design and implement new algorithms for new problems, using time-tested design principles and techniques. * Speed up implementations, using a plethora of tools for high-performance computing in Python. Who this book is for The book is intended for Python programmers who need to learn about algorithmic problem-solving, or who need a refresher. Students of computer science, or similar programming-related topics, such as bioinformatics, may also find the book to be quite useful. Table of Contents * Introduction * The Basics * Counting 101 * Induction and Recursion ...and Reduction * Traversal: The Skeleton Key of Algorithmics * Divide, Combine, and Conquer * Greed Is Good? Prove It! * Tangled Dependencies and Memoization * From A to B with Edsger and Friends * Matchings, Cuts, and Flows * Hard Problems and (Limited) Sloppiness
2016-05-28
Natural Language Processing with Python
This book offers a highly accessible introduction to Natural Language Processing, the field that underpins a variety of language technologies, ranging from predictive text and email filtering to automatic summarization and translation. With Natural Language Processing with Python, you'll learn how to write Python programs to work with large collections of unstructured text. You'll access richly-annotated datasets using a comprehensive range of linguistic data structures. And you'll understand the main algorithms for analyzing the content and structure of written communication., Packed with examples and exercises, Natural Language Processing with Python will help you:, * Extract information from unstructured text, to guess the topic or identify 'named entities', * Analyze linguistic structure in text, including parsing and semantic analysis, * Access popular linguistic databases, including WordNet and treebanks, * Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence, Perfect for individual study, or as a classroom and workshop textbook, this book will help you gain practical skills in Natural Language Processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library., If you're interested in developing Web applications, analyzing multilingual news sources, documenting endangered languages, or if you are simply curious to have a programmer's perspective on how human language works, you will find Natural Language Processing with Python both fascinating and immensely useful.
2016-05-28
Machine Learning in Action
It's been said that data is the new 'dirt'—the raw material from which and on which you build the structures of the modern world. And like dirt, data can seem like a limitless, undifferentiated mass. The ability to take raw data, access it, filter it, process it, visualize it, understand it, and communicate it to others is possibly the most essential business problem for the coming decades., 'Machine learning,' the process of automating tasks once considered the domain of highly-trained analysts and mathematicians, is the key to efficiently extracting useful information from this sea of raw data. By implementing the core algorithms of statistical data processing, data analysis, and data visualization as reusable computer code, you can scale your capacity for data analysis well beyond the capabilities of individual knowledge workers., Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. In it, you'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification., As you work through the numerous examples, you'll explore key topics like classification, numeric prediction, and clustering. Along the way, you'll be introduced to important established algorithms, such as Apriori, through which you identify association patterns in large datasets and Adaboost, a meta-algorithm that can increase the efficiency of many machine learning tasks.
2016-05-28
Learning Robotics using Python
About This Book, Design, simulate, build and program an interactive autonomous mobile robotProgram Robot Operating System using PythonGet a grip on the hands-on guide to robotics for learning various robotics concepts and build an advanced robot from scratch, Who This Book Is For, If you are an engineer, a researcher, or a hobbyist, and you are interested in robotics and want to build your own robot, this book is for you. Readers are assumed to be new to robotics but should have experience with Python., What You Will Learn, Understand the core concepts and terminologies of robotics Create 2D and 3D drawings of robots using freeware such as LibreCAD and Blender Simulate your robot using ROS and Gazebo Build robot hardware from the requirements Explore a diverse range of actuators and its interfacing Interface various robotic sensors to robots Set up and program OpenCV, OpenNI, and PCL to process 2D/3D visual data Learn speech processing and synthesis using Python Apply artificial intelligence to robots using Python Build a robot control GUI using Qt and Python Calibration and testing of robot, In Detail, Learning about robotics will become an increasingly essential skill as it becomes a ubiquitous part of life. Even though robotics is a complex subject, several other tools along with Python can help you design a project to create an easy-to-use interface., Learning Robotics Using Python is an essential guide for creating an autonomous mobile robot using popular robotic software frameworks such as ROS using Python. It also discusses various robot software frameworks and how to go about coding the robot using Python and its framework. It concludes with creating a GUI-based application to control the robot using buttons and slides., By the end of this tutorial, you'll have a clear idea of how to integrate and assemble all things into a robot and how to bundle the software package.
2016-05-28
IPython Interactive Computing and Visualization Cookbook
A comprehensive tutorial covering the state-of-the-art methods you can utilize in IPython, including various real-world examples to help you learn in a practical and effective manner. The book illustrates topics in applied mathematics, scientific modeling, and statistical learning., This book is intended for anyone who wants to use Python as a scripting language for general purpose programming and data analysis. You will find the book particularly useful if you're an experienced Python user looking to take the next step, or if you want to quickly get up to speed with IPython's unique features for data visualization.
2016-05-28
Introduction to Computer Science Using Python
Introduction to Computer Science Using Python: A Computational Problem-Solving Focus introduces students to programming and computational problem-solving via a back-to-basics, step-by-step, objects-late approach that makes this book easy to teach and learn from. Students are provided with a thorough conceptual grounding in computational problem solving before introducing them to specific Python syntax, thus giving them the background to become successful programmers in any language. Dierbach also offers students a thorough grounding in imperative programming before introducing them to object-oriented programming. His step-by-step pedagogical approach makes this an accessible and student-friendly introduction to programming that eases students into program-writing through a variety of hands-on exercises.
2016-05-28
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