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Author: Chris Riccomini
Publisher: No Starch Press
ISBN: 1718501846
Category : Computers
Languages : en
Pages : 194
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Book Description
Key concepts and best practices for new software engineers — stuff critical to your workplace success that you weren’t taught in school. For new software engineers, knowing how to program is only half the battle. You’ll quickly find that many of the skills and processes key to your success are not taught in any school or bootcamp. The Missing README fills in that gap—a distillation of workplace lessons, best practices, and engineering fundamentals that the authors have taught rookie developers at top companies for more than a decade. Early chapters explain what to expect when you begin your career at a company. The book’s middle section expands your technical education, teaching you how to work with existing codebases, address and prevent technical debt, write production-grade software, manage dependencies, test effectively, do code reviews, safely deploy software, design evolvable architectures, and handle incidents when you’re on-call. Additional chapters cover planning and interpersonal skills such as Agile planning, working effectively with your manager, and growing to senior levels and beyond. You’ll learn: How to use the legacy code change algorithm, and leave code cleaner than you found it How to write operable code with logging, metrics, configuration, and defensive programming How to write deterministic tests, submit code reviews, and give feedback on other people’s code The technical design process, including experiments, problem definition, documentation, and collaboration What to do when you are on-call, and how to navigate production incidents Architectural techniques that make code change easier Agile development practices like sprint planning, stand-ups, and retrospectives This is the book your tech lead wishes every new engineer would read before they start. By the end, you’ll know what it takes to transition into the workplace–from CS classes or bootcamps to professional software engineering.
Author: Chris Riccomini
Publisher: No Starch Press
ISBN: 1718501846
Category : Computers
Languages : en
Pages : 194
Get Book Here
Book Description
Key concepts and best practices for new software engineers — stuff critical to your workplace success that you weren’t taught in school. For new software engineers, knowing how to program is only half the battle. You’ll quickly find that many of the skills and processes key to your success are not taught in any school or bootcamp. The Missing README fills in that gap—a distillation of workplace lessons, best practices, and engineering fundamentals that the authors have taught rookie developers at top companies for more than a decade. Early chapters explain what to expect when you begin your career at a company. The book’s middle section expands your technical education, teaching you how to work with existing codebases, address and prevent technical debt, write production-grade software, manage dependencies, test effectively, do code reviews, safely deploy software, design evolvable architectures, and handle incidents when you’re on-call. Additional chapters cover planning and interpersonal skills such as Agile planning, working effectively with your manager, and growing to senior levels and beyond. You’ll learn: How to use the legacy code change algorithm, and leave code cleaner than you found it How to write operable code with logging, metrics, configuration, and defensive programming How to write deterministic tests, submit code reviews, and give feedback on other people’s code The technical design process, including experiments, problem definition, documentation, and collaboration What to do when you are on-call, and how to navigate production incidents Architectural techniques that make code change easier Agile development practices like sprint planning, stand-ups, and retrospectives This is the book your tech lead wishes every new engineer would read before they start. By the end, you’ll know what it takes to transition into the workplace–from CS classes or bootcamps to professional software engineering.
Author: Terry McDonell
Publisher: Vintage
ISBN: 1101970510
Category : Biography & Autobiography
Languages : en
Pages : 386
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Book Description
An Amazon Best Book of 2016 A celebration of the writing and editing life, as well as a look behind the scenes at some of the most influential magazines in America (and the writers who made them what they are). You might not know Terry McDonell, but you certainly know his work. Among the magazines he has top-edited: Outside, Rolling Stone, Esquire, and Sports Illustrated. In this revealing memoir, McDonell talks about what really happens when editors and writers work with deadlines ticking (or drinks on the bar). His stories about the people and personalities he’s known are both heartbreaking and bitingly funny—playing “acid golf” with Hunter S. Thompson, practicing brinksmanship with David Carr and Steve Jobs, working the European fashion scene with Liz Tilberis, pitching TV pilots with Richard Price. Here, too, is an expert’s practical advice on how to recruit—and keep—high-profile talent; what makes a compelling lede; how to grow online traffic that translates into dollars; and how, in whatever format, on whatever platform, a good editor really works, and what it takes to write well. Taking us from the raucous days of New Journalism to today’s digital landscape, McDonell argues that the need for clear storytelling from trustworthy news sources has never been stronger. Says Jeffrey Eugenides: “Every time I run into Terry, I think how great it would be to have dinner with him. Hear about the writers he's known and edited over the years, what the magazine business was like back then, how it's changed and where it's going, inside info about Edward Abbey, Jim Harrison, Annie Proulx, old New York, and the Swimsuit issue. That dinner is this book.”
Author: Sebastian Raschka
Publisher: Packt Publishing Ltd
ISBN: 1801816387
Category : Computers
Languages : en
Pages : 775
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Book Description
This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn Explore frameworks, models, and techniques for machines to learn from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.
Author: Matthew MacDonald
Publisher: "O'Reilly Media, Inc."
ISBN: 1449302394
Category : Computers
Languages : en
Pages : 450
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Book Description
Bestselling author MacDonald shows readers how to best use HTML5's new features to create an effective Web experience for visitors.
Author: Avram Joel Spolsky
Publisher: Apress
ISBN: 1430209887
Category : Computers
Languages : en
Pages : 292
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Book Description
Joel, Apress, Blogs, and Blooks ...I was learning the hard way about how to be a publisher and probably spending way too much time looking at web sites and programming than I should have in response to that. Anyway, one day I came across this web site called , which was run by a guy with strong opinions and an unusual, clever writing style, along with a willingness to take on the conventional wisdom. In particular, he was writing this ongoing series about how bad most user interfaces were—mostly because programmers by and large knew, as Joel and I would say, using the same Yiddish–derived NYC vernacular that we both share, “bupkis” about what users really want. And I, like many, was hooked both by the series and the occasional random essay that Joel wrote. And then I had this epiphany: I'm a publisher, I like reading his stuff, why not turn it into a book?... Read the complete Foreword — Gary Cornell, Cofounder, Apress Since the release of the bestselling title Joel on Software in 2004, requests for a sequel have been relentless. So, we went back to the famed JoelonSoftware.com archives and pulled out a new batch of favorites, many of which have been downloaded over one million times. With Joel's newest book, More Joel on Software, you'll get an even better (not to mention updated) feast of Joel's opinions and impressions on software development, software design, running a software business, and so much more. This is a new selection of essays from the author's web site, http://www.joelonsoftware.com. Joel Spolsky started his weblog in March 2000 in order to offer his insights, based on years of experience, on how to improve the world of programming. This weblog has become infamous among the programming world, and is linked to more than 600 other web sites and translated into 30+ languages! Spolsky's extraordinary writing skills, technical knowledge, and caustic wit have made him a programming guru. With the success of Joel on Software, there has been a strong demand for additional gems and advice, and this book is the answer to those requests. Containing a collection of all–new articles from the original, More Joel on Software has even more of an edge than the original, and the tips for running a business or managing people have far broader application than the software industry. We feel it is safe to say that this is the most useful book you will buy this year.
Author: Brett McLaughlin
Publisher: "O'Reilly Media, Inc."
ISBN: 1449355544
Category : Computers
Languages : en
Pages : 549
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Book Description
If you can build websites with CSS and JavaScript, this book takes you to the next level—creating dynamic, database-driven websites with PHP and MySQL. Learn how to build a database, manage your content, and interact with users. With step-by-step tutorials, this completely revised edition gets you started with expanded coverage of the basics and takes you deeper into the world of server-side programming. The important stuff you need to know: Get up to speed quickly. Learn how to install PHP and MySQL, and get them running on both your computer and a remote server. Gain new techniques. Take advantage of the all-new chapter on integrating PHP with HTML web pages. Manage your content. Use the file system to access user data, including images and other binary files. Make it dynamic. Create pages that change with each new viewing. Build a good database. Use MySQL to store user information and other data. Keep your site working. Master the tools for fixing things that go wrong. Control operations. Create an administrative interface to oversee your site.
Author: Ronald T. Kneusel
Publisher: No Starch Press
ISBN: 1718500742
Category : Computers
Languages : en
Pages : 463
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Book Description
Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects. If you’ve been curious about artificial intelligence and machine learning but didn’t know where to start, this is the book you’ve been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning teaches you the why of deep learning and will inspire you to explore further. All you need is basic familiarity with computer programming and high school math—the book will cover the rest. After an introduction to Python, you’ll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models’ performance. You’ll also learn: How to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector Machines How neural networks work and how they’re trained How to use convolutional neural networks How to develop a successful deep learning model from scratch You’ll conduct experiments along the way, building to a final case study that incorporates everything you’ve learned. The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning will give you the skills and confidence to dive into your own machine learning projects.
Author: Lyn Richards
Publisher: SAGE
ISBN: 1412998069
Category : Reference
Languages : en
Pages : 337
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Book Description
This text offers current thinking in the field. The authors are well-established qualitative researchers and have pulled off a great text for the beginning researcher.
Author: Neal Stephenson
Publisher: Harper Collins
ISBN: 006210134X
Category : Fiction
Languages : en
Pages : 920
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Book Description
“Stephenson has a once-in-a-generation gift: he makes complex ideas clear, and he makes them funny, heartbreaking, and thrilling.” —Time The #1 New York Times bestselling author of Anathem, Neal Stephenson is continually rocking the literary world with his brazen and brilliant fictional creations—whether he’s reimagining the past (The Baroque Cycle), inventing the future (Snow Crash), or both (Cryptonomicon). With Reamde, this visionary author whose mind-stretching fiction has been enthusiastically compared to the work of Thomas Pynchon, Don DeLillo, Kurt Vonnegut, and David Foster Wallace—not to mention William Gibson and Michael Crichton—once again blazes new ground with a high-stakes thriller that will enthrall his loyal audience, science and science fiction, and espionage fiction fans equally. The breathtaking tale of a wealthy tech entrepreneur caught in the very real crossfire of his own online fantasy war game, Reamde is a new high—and a new world—for the remarkable Neal Stephenson.
Author: Christoph Molnar
Publisher: Lulu.com
ISBN: 0244768528
Category : Computers
Languages : en
Pages : 320
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Book Description
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
