Python Algorithms

Author: Magnus Lie Hetland
Editor: Apress
ISBN: 1484200551
Size: 19,63 MB
Format: PDF, Docs
Read: 731
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Python Algorithms, Second Edition 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 in a highly readable manner. It 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.

Graphic Python Algorithms

Author: Yang Hu
Editor:
ISBN:
Size: 12,44 MB
Format: PDF, ePub, Docs
Read: 573
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Algorithms are the heart and soul of computer science. Their applications range from network routing and computational genomics to public-key cryptography and database system implementation. Studying algorithms can make you a better programmer, a clearer thinker, and a master of technical interviews. The book covers a broad range of algorithms , yet makes their design and analysis accessible to all levels of readers. Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and Graphic.

Data Structures And Algorithms With Python

Author: Kent D. Lee
Editor: Springer
ISBN: 3319130722
Size: 12,96 MB
Format: PDF, Docs
Read: 339
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This textbook explains the concepts and techniques required to write programs that can handle large amounts of data efficiently. Project-oriented and classroom-tested, the book presents a number of important algorithms supported by examples that bring meaning to the problems faced by computer programmers. The idea of computational complexity is also introduced, demonstrating what can and cannot be computed efficiently so that the programmer can make informed judgements about the algorithms they use. Features: includes both introductory and advanced data structures and algorithms topics, with suggested chapter sequences for those respective courses provided in the preface; provides learning goals, review questions and programming exercises in each chapter, as well as numerous illustrative examples; offers downloadable programs and supplementary files at an associated website, with instructor materials available from the author; presents a primer on Python for those from a different language background.

Annotated Algorithms In Python

Author: Massimo Di Pierro
Editor: Experts4solutions
ISBN: 9780991160402
Size: 11,16 MB
Format: PDF, ePub, Docs
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This book is assembled from lectures given by the author over a period of 10 years at the School of Computing of DePaul University. The lectures cover multiple classes, including Analysis and Design of Algorithms, Scientific Computing, Monte Carlo Simulations, and Parallel Algorithms. These lectures teach the core knowledge required by any scientist interested in numerical algorithms and by students interested in computational finance.

The Python Workshop

Author: Andrew Bird
Editor: Packt Publishing Ltd
ISBN: 1838984534
Size: 11,22 MB
Format: PDF
Read: 103
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Cut through the noise and get real results with a step-by-step approach to learning Python 3.X programming Key Features Ideal for the Python beginner who is getting started for the first time A step-by-step Python tutorial with exercises and activities that help build key skills Structured to let you progress at your own pace, on your own terms Use your physical print copy to redeem free access to the online interactive edition Book Description You already know you want to learn Python, and a smarter way to learn Python 3 is to learn by doing. The Python Workshop focuses on building up your practical skills so that you can work towards building up your machine learning skills as a data scientist, write scripts that help automate your life and save you time, or even create your own games and desktop applications. You'll learn from real examples that lead to real results. Throughout The Python Workshop, you'll take an engaging step-by-step approach to understanding Python. You won't have to sit through any unnecessary theory. If you're short on time you can jump into a single exercise each day or spend an entire weekend learning about Python scripting. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding. Every physical print copy of The Python Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive free content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your Python book. Fast-paced and direct, The Python Workshop is the ideal companion for Python beginners. You'll build and iterate on your code like a software developer, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead. What you will learn Learn how to write clean and concise code with Python 3 Understand classes and object-oriented programming Tackle entry-level data science and create engaging visualizations Use Python to create responsive, modern web applications Automate essential day-to-day tasks with Python scripts Get started with predictive Python machine learning Who this book is for This book is designed for professionals, students, and hobbyists who want to learn Python and apply it to solve challenging real-world problems. Although this is a beginner’s book, it will help if you already know standard programming topics, such as variables, if-else statements, and functions. Experience with another object-oriented program is beneficial, but not mandatory.

Data Structures And Algorithms In Python

Author: Michael T. Goodrich
Editor: Wiley
ISBN: 1118290275
Size: 11,50 MB
Format: PDF, Mobi
Read: 489
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Based on the authors’ market leading data structures booksin Java and C++, this book offers a comprehensive, definitiveintroduction to data structures in Python by authoritative authors.Data Structures and Algorithms in Python is the firstauthoritative object-oriented book available for Python datastructures. Designed to provide a comprehensive introduction todata structures and algorithms, including their design, analysis,and implementation, the text will maintain the same generalstructure as Data Structures and Algorithms in Java andData Structures and Algorithms in C++. Begins by discussing Python’s conceptually simple syntax,which allows for a greater focus on concepts. Employs a consistent object-oriented viewpoint throughout thetext. Presents each data structure using ADTs and their respectiveimplementations and introduces important design patterns as a meansto organize those implementations into classes, methods, andobjects. Provides a thorough discussion on the analysis and design offundamental data structures. Includes many helpful Python code examples, with source codeprovided on the website. Uses illustrations to present data structures and algorithms,as well as their analysis, in a clear, visual manner. Provides hundreds of exercises that promote creativity, helpreaders learn how to think like programmers, and reinforceimportant concepts. Contains many Python-code and pseudo-code fragments, andhundreds of exercises, which are divided into roughly 40%reinforcement exercises, 40% creativity exercises, and 20%programming projects.

Reinforcement Learning Algorithms With Python

Author: Andrea Lonza
Editor: Packt Publishing Ltd
ISBN: 1789139708
Size: 20,13 MB
Format: PDF, Kindle
Read: 159
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Develop self-learning algorithms and agents using TensorFlow and other Python tools, frameworks, and libraries Key Features Learn, develop, and deploy advanced reinforcement learning algorithms to solve a variety of tasks Understand and develop model-free and model-based algorithms for building self-learning agents Work with advanced Reinforcement Learning concepts and algorithms such as imitation learning and evolution strategies Book Description Reinforcement Learning (RL) is a popular and promising branch of AI that involves making smarter models and agents that can automatically determine ideal behavior based on changing requirements. This book will help you master RL algorithms and understand their implementation as you build self-learning agents. Starting with an introduction to the tools, libraries, and setup needed to work in the RL environment, this book covers the building blocks of RL and delves into value-based methods, such as the application of Q-learning and SARSA algorithms. You'll learn how to use a combination of Q-learning and neural networks to solve complex problems. Furthermore, you'll study the policy gradient methods, TRPO, and PPO, to improve performance and stability, before moving on to the DDPG and TD3 deterministic algorithms. This book also covers how imitation learning techniques work and how Dagger can teach an agent to drive. You'll discover evolutionary strategies and black-box optimization techniques, and see how they can improve RL algorithms. Finally, you'll get to grips with exploration approaches, such as UCB and UCB1, and develop a meta-algorithm called ESBAS. By the end of the book, you'll have worked with key RL algorithms to overcome challenges in real-world applications, and be part of the RL research community. What you will learn Develop an agent to play CartPole using the OpenAI Gym interface Discover the model-based reinforcement learning paradigm Solve the Frozen Lake problem with dynamic programming Explore Q-learning and SARSA with a view to playing a taxi game Apply Deep Q-Networks (DQNs) to Atari games using Gym Study policy gradient algorithms, including Actor-Critic and REINFORCE Understand and apply PPO and TRPO in continuous locomotion environments Get to grips with evolution strategies for solving the lunar lander problem Who this book is for If you are an AI researcher, deep learning user, or anyone who wants to learn reinforcement learning from scratch, this book is for you. You’ll also find this reinforcement learning book useful if you want to learn about the advancements in the field. Working knowledge of Python is necessary.

Python In A Nutshell

Author: Alex Martelli
Editor: "O'Reilly Media, Inc."
ISBN: 149191386X
Size: 19,23 MB
Format: PDF, ePub
Read: 177
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This book provides a quick reference to the language, including Python 3.5, 2.7 and highlights of 3.6. It covers a wide range of application areas, including web and network programming, XML handling, database interactions, and high-speed numeric computing.

Python Data Structures And Algorithms

Author: Benjamin Baka
Editor: Packt Publishing Ltd
ISBN: 1786465337
Size: 11,67 MB
Format: PDF, Kindle
Read: 458
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Implement classic and functional data structures and algorithms using Python About This Book A step by step guide, which will provide you with a thorough discussion on the analysis and design of fundamental Python data structures. Get a better understanding of advanced Python concepts such as big-o notation, dynamic programming, and functional data structures. Explore illustrations to present data structures and algorithms, as well as their analysis, in a clear, visual manner. Who This Book Is For The book will appeal to Python developers. A basic knowledge of Python is expected. What You Will Learn Gain a solid understanding of Python data structures. Build sophisticated data applications. Understand the common programming patterns and algorithms used in Python data science. Write efficient robust code. In Detail Data structures allow you to organize data in a particular way efficiently. They are critical to any problem, provide a complete solution, and act like reusable code. In this book, you will learn the essential Python data structures and the most common algorithms. With this easy-to-read book, you will be able to understand the power of linked lists, double linked lists, and circular linked lists. You will be able to create complex data structures such as graphs, stacks and queues. We will explore the application of binary searches and binary search trees. You will learn the common techniques and structures used in tasks such as preprocessing, modeling, and transforming data. We will also discuss how to organize your code in a manageable, consistent, and extendable way. The book will explore in detail sorting algorithms such as bubble sort, selection sort, insertion sort, and merge sort. By the end of the book, you will learn how to build components that are easy to understand, debug, and use in different applications. Style and Approach The easy-to-read book with its fast-paced nature will improve the productivity of Python programmers and improve the performance of Python applications.

Bioinformatics Algorithms

Author: Miguel Rocha
Editor: Academic Press
ISBN: 0128125217
Size: 10,37 MB
Format: PDF, Docs
Read: 741
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Bioinformatics Algorithms: Design and Implementation in Python provides a comprehensive book on many of the most important bioinformatics problems, putting forward the best algorithms and showing how to implement them. The book focuses on the use of the Python programming language and its algorithms, which is quickly becoming the most popular language in the bioinformatics field. Readers will find the tools they need to improve their knowledge and skills with regard to algorithm development and implementation, and will also uncover prototypes of bioinformatics applications that demonstrate the main principles underlying real world applications. Presents an ideal text for bioinformatics students with little to no knowledge of computer programming Based on over 12 years of pedagogical materials used by the authors in their own classrooms Features a companion website with downloadable codes and runnable examples (such as using Jupyter Notebooks) and exercises relating to the book