Articles by "Python"

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Laura Graesser, Wah Loon Keng ... 416 pages - ISBN-13: 978-0135172384 - ISBN-10: 0135172381 ... Publisher: Addison-Wesley Professional; (December, 2019) - Language: English.

The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice: Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games–such as Go, Atari games, and DotA 2–to robotics.

Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python: Understand each key aspect of a deep RL problem + Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER) + Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO) + Understand how algorithms can be parallelized synchronously and asynchronously + Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work + Explore algorithm benchmark results with tuned hyperparameters + Understand how deep RL environments are designed.

Eihab B. M. Bashier ... 348 pages - Publisher: CRC Press; (March, 2020) ... Language: English - ISBN-10: 0367076691 - ISBN-13: 978-0367076696.

Practical Numerical and Scientific Computing with MATLAB and Python concentrates on the practical aspects of numerical analysis and linear and non-linear programming. It discusses the methods for solving different types of mathematical problems using MATLAB and Python. Although the book focuses on the approximation problem rather than on error analysis of mathematical problems, it provides practical ways to calculate errors. The book is divided into three parts, covering topics in numerical linear algebra, methods of interpolation, numerical differentiation and integration, solutions of differential equations, linear and non-linear programming problems, and optimal control problems. This book has the following advantages: It adopts the programming languages, MATLAB and Python, which are widely used among academics, scientists, and engineers, for ease of use and contain many libraries covering many scientific and engineering fields. + It contains topics that are rarely found in other numerical analysis books, such as ill-conditioned linear systems and methods of regularization to stabilize their solutions, nonstandard finite differences methods for solutions of ordinary differential equations, and the computations of the optimal controls. It provides a practical explanation of how to apply these topics using MATLAB and Python. + It discusses software libraries to solve mathematical problems, such as software Gekko, pulp, and pyomo. These libraries use Python for solutions to differential equations and static and dynamic optimization problems. + Most programs in the book can be applied in versions prior to MATLAB 2017b and Python 3.7.4 without the need to modify these programs. This book is aimed at newcomers and middle-level students, as well as members of the scientific community who are interested in solving math problems using MATLAB or Python.

Erik Lee Nylen, Pascal Wallisch ... 368 pages - Publisher: Academic Press; (April, 2017) ... Language: English - ISBN-10: 0128040432 - ISBN-13: 978-0128040430.

A Primer with MATLAB® and Python™ present important information on the emergence of the use of Python, a more general purpose option to MATLAB, the preferred computation language for scientific computing and analysis in neuroscience. This book addresses the snake in the room by providing a beginner’s introduction to the principles of computation and data analysis in neuroscience, using both Python and MATLAB, giving readers the ability to transcend platform tribalism and enable coding versatility.

Zed Shaw ... 320 pages - Publisher: Addison-Wesley Professional; (June, 2017) ... Language: English - ISBN-10: 0134692888 - ISBN-13: 978-0134692883.

In Learn Python 3 the Hard Way, you’ll learn Python by working through 52 brilliantly crafted exercises. Read them. Type their code precisely. (No copying and pasting!) Fix your mistakes. Watch the programs run. As you do, you’ll learn how a computer works; what good programs look like; and how to read, write, and think about code. Zed then teaches you even more in 5+ hours of video where he shows you how to break, fix, and debug your code—live, as he’s doing the exercises.

Install a complete Python environment + Organize and write code + Fix and break code
Basic mathematics + Variables + Strings and text + Interact with users + Work with files + Looping and logic + Data structures using lists and dictionaries + Program design + Object-oriented programming + Inheritance and composition + Modules, classes, and objects + Python packaging + Automated testing + Basic game development + Basic web development.

It’ll be hard at first. But soon, you’ll just get it—and that will feel great! This course will reward you for every minute you put into it. Soon, you’ll know one of the world’s most powerful, popular programming languages. You’ll be a Python programmer.  This Book Is Perfect For: Total beginners with zero programming experience + Junior developers who know one or two languages + Returning professionals who haven’t written code in years + Seasoned professionals looking for a fast, simple, crash course in Python 3.

Mark Fenner ... 592 pages - Publisher: Addison-Wesley Professional; (August, 2019) ... Language: English - ISBN-10: 0134845625 - ISBN-13: 978-0134845623.

The Complete Beginner’s Guide to Understanding and Building Machine Learning Systems with Python: Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you’re an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning. Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you’ll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field’s most sophisticated and exciting techniques. Whether you’re a student, analyst, scientist, or hobbyist, this guide’s insights will be applicable to every learning system you ever build or use.

Understand machine learning algorithms, models, and core machine learning concepts + Classify examples with classifiers, and quantify examples with regressors + Realistically assess performance of machine learning systems + Use feature engineering to smooth rough data into useful forms + Chain multiple components into one system and tune its performance + Apply machine learning techniques to images and text + Connect the core concepts to neural networks and graphical models + Leverage the Python scikit-learn library and other powerful tools.

Benjamin Bengfort, Rebecca Bilbro, Tony Ojeda ... 332 pages - Publisher: O'Reilly Media; (July, 2018) ... Language: English - ISBN-10: 1491963042 - ISBN-13: 978-1491963043.

From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist’s approach to building language-aware products with applied machine learning. You’ll learn robust, repeatable, and scalable techniques for text analysis with Python, including contextual and linguistic feature engineering, vectorization, classification, topic modeling, entity resolution, graph analysis, and visual steering. By the end of the book, you’ll be equipped with practical methods to solve any number of complex real-world problems.

Preprocess and vectorize text into high-dimensional feature representations + Perform document classification and topic modeling + Steer the model selection process with visual diagnostics + Extract key phrases, named entities, and graph structures to reason about data in text + Build a dialog framework to enable chatbots and language-driven interaction + Use Spark to scale processing power and neural networks to scale model complexity.

Ryan Mitchell ... 308 pages - Publisher: O'Reilly Media; 2nd edition (April, 2018) ... Language: English - ISBN-10: 1491985577 - ISBN-13: 978-1491985571.

If programming is magic then web scraping is surely a form of wizardry. By writing a simple automated program, you can query web servers, request data, and parse it to extract the information you need. The expanded edition of this practical book not only introduces you web scraping, but also serves as a comprehensive guide to scraping almost every type of data from the modern web. Part I focuses on web scraping mechanics: using Python to request information from a web server, performing basic handling of the server’s response, and interacting with sites in an automated fashion. Part II explores a variety of more specific tools and applications to fit any web scraping scenario you’re likely to encounter. Parse complicated HTML pages + Develop crawlers with the Scrapy framework + Learn methods to store data you scrape + Read and extract data from documents + Clean and normalize badly formatted data + Read and write natural languages + Crawl through forms and logins + Scrape JavaScript and crawl through APIs + Use and write image-to-text software + Avoid scraping traps and bot blockers + Use scrapers to test your website.

Ben Stephenson ... 219 pages - Publisher: Springer; 2nd edition (July, 2019) ... Language: English - ISBN-10: 3030188728 - ISBN-13: 978-3030188726.

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 186 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. This enhanced new edition has been thoroughly updated and expanded with additional exercises, along with concise introductions that outline the core concepts needed to solve them. The exercises and solutions require no prior background knowledge, beyond the material covered in a typical introductory Python programming course.

Features: Uses an accessible writing style and easy-to-follow structure; includes a mixture of classic exercises from the fields of computer science and mathematics, along with exercises that connect to other academic disciplines; presents the solutions to approximately half of the exercises; provides annotations alongside the solutions, which explain the approach taken to solve the problem and relevant aspects of Python syntax; offers a variety of exercises of different lengths and difficulties; contains exercises that encourage the development of programming skills using if statements, loops, basic functions, lists, dictionaries, files, and recursive functions. Undergraduate students enrolled in their first programming course and wishing to enhance their programming abilities will find the exercises and solutions provided in this book to be ideal for their needs.

Seth Weidman ... 253 pages - Publisher: O'Reilly Media; (September, 2019) ... Language: English - AmazonSIN: B07XL53Y4C.

With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. You’ll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way. Author Seth Weidman shows you how neural networks work using a first principles approach. You’ll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural networks work mathematically, computationally, and conceptually, you’ll be set up for success on all future deep learning projects.

This book provides: Extremely clear and thorough mental models—accompanied by working code examples and mathematical explanations—for understanding neural networks + Methods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented framework + Working implementations and clear-cut explanations of convolutional and recurrent neural networks + Implementation of these neural network concepts using the popular PyTorch framework.

Eugene Charniak ... 192 pages - Publisher: The MIT Press; (January, 2019) ... Language: English - AmazonSIN: B07PGRZXN8.

This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open-source machine learning framework. “I find I learn computer science material best by sitting down and writing programs,” the author writes, and the book reflects this approach.

Each chapter includes a programming project, exercises, and references for further reading. An early chapter is devoted to Tensorflow and its interface with Python, the widely used programming language. Familiarity with linear algebra, multivariate calculus, and probability and statistics is required, as is a rudimentary knowledge of programming in Python. The book can be used in both undergraduate and graduate courses; practitioners will find it an essential reference.

Jesús Rogel-Salazar ... 420 pages - Publisher: Chapman and Hall/CRC; (May, 2020) ... Language: English - AmazonSIN: B0883XB13B.

Advanced Data Science and Analytics with Python enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow-up to the topics discussed in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK, NetworkX and others. The model development is supported by the use of frameworks such as Keras, TensorFlow and Core ML, as well as Swift for the development of iOS and MacOS applications.

Features: Targets readers with a background in programming, who are interested in the tools used in data analytics and data science + Uses Python throughout + Presents tools, alongside solved examples, with steps that the reader can easily reproduce and adapt to their needs + Focuses on the practical use of the tools rather than on lengthy explanations + Provides the reader with the opportunity to use the book whenever needed rather than following a sequential path.

The book can be read independently from the previous volume and each of the chapters in this volume is sufficiently independent from the others, providing flexibility for the reader. Each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book. Time series analysis, natural language processing, topic modelling, social network analysis, neural networks and deep learning are comprehensively covered. The book discusses the need to develop data products and addresses the subject of bringing models to their intended audiences – in this case, literally to the users’ fingertips in the form of an iPhone app.

Allen B. Downey ... 210 pages - Publisher: O'Reilly Media; (October, 2013) ... Language: English - ISBN-10: 1449370780 - ISBN-13: 978-1449370787.

If you know how to program with Python and also know a little about probability, you’re ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical notation, and use discrete probability distributions instead of continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer, and you’ll begin to apply these techniques to real-world problems. Bayesian statistical methods are becoming more common and more important, but not many resources are available to help beginners. Based on undergraduate classes taught by author Allen Downey, this book’s computational approach helps you get a solid start. Use your existing programming skills to learn and understand Bayesian statistics + Work with problems involving estimation, prediction, decision analysis, evidence, and hypothesis testing + Get started with simple examples, using coins, M&Ms, Dungeons & Dragons dice, paintball, and hockey + Learn computational methods for solving real-world problems, such as interpreting SAT scores, simulating kidney tumors, and modeling the human microbiome.

Laura Igual, Santi Segui ... 218 pages - Publisher: Springer; (March, 2017) ... Language: English - ISBN-10: 3319500163 - ISBN-13: 978-331950016.

This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website.

John M. Stewart ... 230 pages - Publisher: Cambridge University Press; (August, 2014) ... Language: English - ISBN-10: 1107686423 - ISBN-13: 978-1107686427.

Python is a free, open source, easy-to-use software tool that offers a significant alternative to proprietary packages such as MATLAB and Mathematica. This book covers everything the working scientist needs to know to start using Python effectively. The author explains scientific Python from scratch, showing how easy it is to implement and test non-trivial mathematical algorithms and guiding the reader through the many freely available add-on modules. A range of examples, relevant to many different fields, illustrate the program's capabilities. In particular, readers are shown how to use pre-existing legacy code (usually in Fortran77) within the Python environment, thus avoiding the need to master the original code. Instead of exercises the book contains useful snippets of tested code which the reader can adapt to handle problems in their own field, allowing students and researchers with little computer expertise to get up and running as soon as possible.

Micha Gorelick, Ian Ozsvald ... 370 pages- Publisher: O'Reilly Media; (September, 2014) ... Language: English - ISBN-10: 1449361595 - ISBN-13: 978-1449361594. 

Your Python code may run correctly, but you need it to run faster. By exploring the fundamental theory behind design choices, this practical guide helps you gain a deeper understanding of Python’s implementation. You’ll learn how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. How can you take advantage of multi-core architectures or clusters? Or build a system that can scale up and down without losing reliability? Experienced Python programmers will learn concrete solutions to these and other issues, along with war stories from companies that use high performance Python for social media analytics, productionized machine learning, and other situations.

Get a better grasp of numpy, Cython and profilers + Learn how Python abstracts the underlying computer architecture + Use profiling to find bottlenecks in CPU time and memory usage + Write efficient programs by choosing appropriate data structures + Speed up matrix and vector computations + Use tools to compile Python down to machine code + Manage multiple I/O and computational operations concurrently + Convert multiprocessing code to run on a local or remote cluster + Solve large problems while using less RAM.

Oliver R. Simpson ... 160 pages - Publisher: UnKnown; (November, 2019) ... Language: English - ASIN: B081JBHPLV by Amazon.

If your dream is to become a Python pro, or you always want to code like a boss, solve real-world problems, build web apps, or even automate repetitive tasks, then you’ve come to the right place. By the end of this book, you will have mastered the fundamentals of Python language. You will learn how to create programs that will save you time and simplify your life. Python is a straightforward language, so even if this is your first time to learn any programming language, you can learn Python without experiencing any issues. Python has multiple applications, and so you've got a high probability of getting a great job once you become a pro in Python language.

Whether it’s in web development, data analysis, scripting, and machine learning, all require that you be experienced in Python. This is a beginner guide that will take you from absolute scratch to creating your first Python program. Not only that, but you’ll also learn lots of stuff about the Django Python framework. With Django, you will learn how to create web apps very quickly and efficiently.

J. Burton Browning, Marty Alchin ... 384 pages - Publisher: Apress; 2nd edition (December, 2014) ... Language: English - ISBN-10: 1484203356 - ISBN-13: 978-1484203354.

You’ve learned the basics of Python, but how do you take your skills to the next stage? Even if you know enough to be productive, there are a number of features that can take you to the next level in Python. Pro Python, Second Edition explores concepts and features normally left to experimentation, allowing you to be even more productive and creative. In addition to pure code concerns, Pro Python develops your programming techniques and approaches, which will help make you a better Python programmer. This book will improve not only your code but also your understanding and interaction with the many established Python communities. This book takes your Python knowledge and coding skills to the next level. It shows you how to write clean, innovative code that will be respected by your peers. With this book, make your code do more with introspection and meta-programming. And learn and later use the nuts and bolts of an application, tier-by-tier as a complex case study along the way.

What you’ll learn: Write strong Python code that will be respected in the Python community + Understand the reasons behind big design decisions in Python + Write programs that can reconfigure themselves in Python + Disguise your code as different types of objects in Python + Inspect just about any object in Python + Prepare your code for international audiences + Ensure code quality with rigorous testing. Who this book is for: This book is for intermediate to advanced Python programmers who are looking to understand how and why Python works the way it does and how they can take their code to the next level.

Jonathan Yates ... 170 pages - Publisher: UnKnown; (July, 2016) ... Language: English - ASIN: B01I5P72PC by Amazon.

Are you aware that websites like Instagram, Spotify, and Pinterest use Python Programming in to make their sites? Will you create the next Instagram with your newfound expertise in Python? Python Programming is a widely used language that anyone can use and get good with, and also a super concise language that you can create nearly anything with. Mac, Linux, UNIX, and others have Python installed as a default setting since it is an open source and free language. After you read this book, you will fluent in this versatile code language and see it applied to a variety of examples now with pictures! As stated, you can use the language to create everything you want; a website, make a game, or even create a search engine. The big plus of using Python is, an explicit compiler is not necessary since it’s an entirely interpreted language like Perl, Shell, and others.

Connor P. Milliken ... 332 pages - Publisher: Apress; (November, 2019) ... Language: English - ASIN: B081LWB7FK by Amazon.

Immerse yourself in learning Python and introductory data analytics with this book’s project-based approach. Through the structure of a ten-week coding bootcamp course, you’ll learn key concepts and gain hands-on experience through weekly projects. Each chapter in this book is presented as a full week of topics, with Monday through Thursday covering specific concepts, leading up to Friday, when you are challenged to create a project using the skills learned throughout the week. Topics include Python basics and essential intermediate concepts such as list comprehension, generators and iterators, understanding algorithmic complexity, and data analysis with pandas. From beginning to end, this book builds up your abilities through exercises and challenges, culminating in your solid understanding of Python.

Challenge yourself with the intensity of a coding bootcamp experience or learn at your own pace. With this hands-on learning approach, you will gain the skills you need to jumpstart a new career in programming or further your current one as a software developer. 

What You Will Learn: Understand beginning and more advanced concepts of the Python language + Be introduced to data analysis using pandas, the Python Data Analysis library + Walk through the process of interviewing and answering technical questions + Create real-world applications with the Python language + Learn how to use Anaconda, Jupyter Notebooks, and the Python Shell. Who This Book Is For: Those trying to jumpstart a new career into programming, and those already in the software development industry and would like to learn Python programming.

Pramod Singh, Avinash Manure ... 164 pages - Publisher: Apress; (December, 2019) ... Language: English - ASIN: B082X9CM42 by Amazon.

Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples: The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0. It also demonstrates how to build models using customer estimators. Further, it explains how to use TensorFlow 2.0 API to build machine learning and deep learning models for image classification using the standard as well as custom parameters. You'll review sequence predictions, saving, serving, deploying, and standardized datasets, and then deploy these models to production. All the code presented in the book will be available in the form of executable scripts at Github which allows you to try out the examples and extend them in interesting ways.

What You'll Learn: Review the new features of TensorFlow 2.0 + Use TensorFlow 2.0 to build machine learning and deep learning models + Perform sequence predictions using TensorFlow 2.0 + Deploy TensorFlow 2.0 models with practical examples

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