manning publications dez 2021 (5 Ergebnisse)

Taschenbuch. Zustand: Neu. Neuware - Your brain responds in a predictable way when it encounters new or difficult tasks. This unique book teaches you concrete techniques rooted in cognitive science that will improve the way you learn and think about code.Summary In The Programmer's Brain: What every programmer needs to know about cognition you will learn: Fast and effective ways to master new programming languages Speed reading skills to quickly comprehend new code Techniques to unravel the meaning of complex code Ways to learn new syntax and keep it memorized Writing code that is easy for others to read Picking the right names for your variables Making your codebase more understandable to newcomers Onboarding new developers to your team Learn how to optimize your brain's natural cognitive processes to read code more easily, write code faster, and pick up new languages in much less time. This book will help you through the confusion you feel when faced with strange and complex code, and explain a codebase in ways that can make a new team member productive in days! Foreword by Jon Skeet. Purchase of the print book includes a free Elektronisches Buch in PDF, Kindle, and ePub formats from Manning Publications. About the technology Take advantage of your brain's natural processes to be a better programmer. Techniques based in cognitive science make it possible to learn new languages faster, improve productivity, reduce the need for code rewrites, and more. This unique book will help you achieve these gains. About the book The Programmer's Brain unlocks the way we think about code. It offers scientifically sound techniques that can radically improve the way you master new technology, comprehend code, and memorize syntax. You'll learn how to benefit from productive struggle and turn confusion into a learning tool. Along the way, you'll discover how to create study resources as you become an expert at teaching yourself and bringing new colleagues up to speed. What's inside Understand how your brain sees code Speed reading skills to learn code quickly Techniques to unravel complex code Tips for making codebases understandable About the reader For programmers who have experience working in more than one language. About the author Dr. Felienne Hermans is an associate professor at Leiden University in the Netherlands. She has spent the last decade researching programming, how to learn and how to teach it. Table of Contents PART 1 ON READING CODE BETTER 1 Decoding your confusion while coding 2 Speed reading for code 3 How to learn programming syntax quickly 4 How to read complex code PART 2 ON THINKING ABOUT CODE 5 Reaching a deeper understanding of code 6 Getting better at solving programming problems 7 Misconceptions: Bugs in thinking PART 3 ON WRITING BETTER CODE 8 How to get better at naming things 9 Avoiding bad code and cognitive load: Two frameworks 10 Getting better at solving complex problems PART 4 ON COLLABORATING ON CODE 11 The act of writing code 12 Designing and improving larger systems 13 How to onboard new developers.

Taschenbuch. Zustand: Neu. Neuware - IN A WORD A field guide for the unique challenges of data science leadership, filled with transformative insights, personal experiences, and industry examples.'Spot-on as a career resource! Captures what's important to be successful as a data scientist." Eric Colson, Former Data Executive at Stitch Fix, Netflix In How To Lead in Data Science you will learn: Best practices for leading projects while balancing complex trade-offs Specifying, prioritizing, and planning projects from vague requirements Navigating structural challenges in your organization Working through project failures with positivity and tenacity Growing your team with coaching, mentoring, and advising Crafting technology roadmaps and championing successful projects Driving diversity, inclusion, and belonging within teams Architecting a long-term business strategy and data roadmap as an executive Delivering a data-driven culture and structuring productive data science organizations How to Lead in Data Science is full of techniques for leading data science at every seniority levelfrom heading up a single project to overseeing a whole company's data strategy. Authors Jike Chong and Yue Cathy Chang share hard-won advice that they've developed building data teams for LinkedIn, Acorns, Yiren Digital, large asset-management firms, Fortune 50 companies, and more. You'll find advice on plotting your long-term career advancement, as well as quick wins you can put into practice right away. Carefully crafted assessments and interview scenarios encourage introspection, reveal personal blind spots, and highlight development areas. Purchase of the print book includes a free Elektronisches Buch in PDF, Kindle, and ePub formats from Manning Publications. About the technology Lead your data science teams and projects to success! To make a consistent, meaningful impact as a data science leader, you must articulate technology roadmaps, plan effective project strategies, support diversity, and create a positive environment for professional growth. This book delivers the wisdom and practical skills you need to thrive as a data science leader at all levels, from team member to the C-suite. About the book How to Lead in Data Science shares unique leadership techniques from high-performance data teams. It's filled with best practices for balancing project trade-offs and producing exceptional results, even when beginning with vague requirements or unclear expectations. You'll find a clearly presented modern leadership framework based on current case studies, with insights reaching all the way to Aristotle and Confucius. As you read, you'll build practical skills to grow and improve your team, your company's data culture, and yourself. What's inside How to coach and mentor team members Navigate an organization's structural challenges Secure commitments from other teams and partners Stay current with the technology landscape Advance your career About the reader For data science practitioners at all levels. About the author Dr. Jike Chong and Yue Cathy Chang build, lead, and grow high-performing data teams across industries in public and private companies, such as Acorns, LinkedIn, large asset-management firms, and Fortune 50 companies. Table of Contents 1 What makes a successful data scientist PART 1 THE TECH LEAD: CULTIVATING LEADERSHIP 2 Capabilities for leading projects 3 Virtues for leading projects PART 2 THE MANAGER: NURTURING A TEAM 4 Capabilities for leading people 5 Virtues for leading people PART 3 THE DIRECTOR: GOVERNING A FUNCTION 6 Capabilities for leading a function 7 Virtues for leading a function PART 4 THE EXECUTIVE: INSPIRING AN INDUSTRY 8 Capabilities for leading a company 9 Virtues for leading a company PART 5 THE LOOP AND THE FUTURE 10 Landscape, organization, opportunity, and practice 11 Leading in data science and a future outlook.

Taschenbuch. Zustand: Neu. Neuware - Software Telemetry shows you how to efficiently collect, store, and analyze system and application log data so you can monitor and improve your systems. Summary In Software Telemetry you will learn how to: Manage toxic telemetry and confidential records Master multi-tenant techniques and transformation processes Update to improve the statistical validity of your metrics and dashboards Make software telemetry emissions easier to parse Build easily-auditable logging systems Prevent and handle accidental data leaks Maintain processes for legal compliance Justify increased spend on telemetry software Software Telemetry teaches you best practices for operating and updating telemetry systems. These vital systems trace, log, and monitor infrastructure by observing and analyzing the events generated by the system. This practical guide is filled with techniques you can apply to any size of organization, with troubleshooting techniques for every eventuality, and methods to ensure your compliance with standards like GDPR. Purchase of the print book includes a free Elektronisches Buch in PDF, Kindle, and ePub formats from Manning Publications. About the technology Take advantage of the data generated by your IT infrastructure! Telemetry systems provide feedback on what's happening inside your data center and applications, so you can efficiently monitor, maintain, and audit them. This practical book guides you through instrumenting your systems, setting up centralized logging, doing distributed tracing, and other invaluable telemetry techniques. About the book Software Telemetry shows you how to efficiently collect, store, and analyze system and application log data so you can monitor and improve your systems. Manage the pillars of observabilitylogs, metrics, and tracesin an end-to-end telemetry system that integrates with your existing infrastructure. You'll discover how software telemetry benefits both small startups and legacy enterprises. And at a time when data audits are increasingly common, you'll appreciate the thorough coverage of legal compliance processes, so there's no reason to panic when a discovery request arrives. What's inside Multi-tenant techniques and transformation processes Toxic telemetry and confidential records Updates to improve the statistical validity of your metrics and dashboards Revisions that make software telemetry emissions easier to parse About the reader For software developers and infrastructure engineers supporting and building telemetry systems. About the author Jamie Riedesel is a staff engineer at Dropbox with over twenty years of experience in IT. Table of Contents 1 Introduction PART 1 TELEMETRY SYSTEM ARCHITECTURE 2 The Emitting stage: Creating and submitting telemetry 3 The Shipping stage: Moving and storing telemetry 4 The Shipping stage: Unifying diverse telemetry formats 5 The Presentation stage: Displaying telemetry 6 Marking up and enriching telemetry 7 Handling multitenancy PART 2 USE CASES REVISITED: APPLYING ARCHITECTURE CONCEPTS 8 Growing cloud-based startup 9 Nonsoftware business 10 Long-established business IT PART 3 TECHNIQUES FOR HANDLING TELEMETRY 11 Optimizing for regular expressions at scale 12 Standardized logging and event formats 13 Using more nonfile emitting techniques 14 Managing cardinality in telemetry 15 Ensuring telemetry integrity 16 Redacting and reprocessing telemetry 17 Building policies for telemetry retention and aggregation 18 Surviving legal processes.

Taschenbuch. Zustand: Neu. Neuware - Real-world Natural Language Processing shows you how to build the practical NLP applications that are transforming the way humans and computers work together.In Real-world Natural Language Processing you will learn how to: Design, develop, and deploy useful NLP applications Create named entity taggers Build machine translation systems Construct language generation systems and chatbots Use advanced NLP concepts such as attention and transfer learning Real-world Natural Language Processing teaches you how to create practical NLP applications without getting bogged down in complex language theory and the mathematics of deep learning. In this engaging book, you'll explore the core tools and techniques required to build a huge range of powerful NLP apps, including chatbots, language detectors, and text classifiers. Purchase of the print book includes a free Elektronisches Buch in PDF, Kindle, and ePub formats from Manning Publications. About the technology Training computers to interpret and generate speech and text is a monumental challenge, and the payoff for reducing labor and improving human/computer interaction is huge! Th e field of Natural Language Processing (NLP) is advancing rapidly, with countless new tools and practices. This unique book offers an innovative collection of NLP techniques with applications in machine translation, voice assistants, text generation, and more. About the book Real-world Natural Language Processing shows you how to build the practical NLP applications that are transforming the way humans and computers work together. Guided by clear explanations of each core NLP topic, you'll create many interesting applications including a sentiment analyzer and a chatbot. Along the way, you'll use Python and open source libraries like AllenNLP and HuggingFace Transformers to speed up your development process. What's inside Design, develop, and deploy useful NLP applications Create named entity taggers Build machine translation systems Construct language generation systems and chatbots About the reader For Python programmers. No prior machine learning knowledge assumed. About the author Masato Hagiwara received his computer science PhD from Nagoya University in 2009. He has interned at Google and Microsoft Research, and worked at Duolingo as a Senior Machine Learning Engineer. He now runs his own research and consulting company. Table of Contents PART 1 BASICS 1 Introduction to natural language processing 2 Your first NLP application 3 Word and document embeddings 4 Sentence classification 5 Sequential labeling and language modeling PART 2 ADVANCED MODELS 6 Sequence-to-sequence models 7 Convolutional neural networks 8 Attention and Transformer 9 Transfer learning with pretrained language models PART 3 PUTTING INTO PRODUCTION 10 Best practices in developing NLP applications 11 Deploying and serving NLP applications.

Taschenbuch. Zustand: Neu. Neuware - Five Lines of Code teaches refactoring that's focused on concrete rules and getting any method down to five lines or less! There's no jargon or tricky automated-testing skills required, just easy guidelines and patterns illustrated by detailed code samples.In Five Lines of Code you will learn: The signs of bad code Improving code safely, even when you don't understand it Balancing optimization and code generality Proper compiler practices The Extract method, Introducing Strategy pattern, and many other refactoring patterns Writing stable code that enables change-by-addition Writing code that needs no comments Real-world practices for great refactoring Improving existing coderefactoringis one of the most common tasks you'll face as a programmer. Five Lines of Code teaches you clear and actionable refactoring rules that you can apply without relying on intuitive judgements such as "code smells." Following the author's expert perspectivethat refactoring and code smells can be learned by following a concrete set of principlesyou'll learn when to refactor your code, what patterns to apply to what problem, and the code characteristics that indicate it's time for a rework. Foreword by Robert C. Martin. Purchase of the print book includes a free Elektronisches Buch in PDF, Kindle, and ePub formats from Manning Publications. About the technology Every codebase includes mistakes and inefficiencies that you need to find and fix. Refactor the right way, and your code becomes elegant, easy to read, and easy to maintain. In this book, you'll learn a unique approach to refactoring that implements any method in five lines or fewer. You'll also discover a secret most senior devs know: sometimes it's quicker to hammer out code and fix it later! About the book Five Lines of Code is a fresh look at refactoring for developers of all skill levels. In it, you'll master author Christian Clausen's innovative approach, learning concrete rules to get any method down to five linesor less! You'll learn when to refactor, specific refactoring patterns that apply to most common problems, and characteristics of code that should be deleted altogether. What's inside The signs of bad code Improving code safely, even when you don't understand it Balancing optimization and code generality Proper compiler practices About the reader For developers of all skill levels. Examples use easy-to-read Typescript, in the same style as Java and C#. About the author Christian Clausen works as a Technical Agile Coach, teaching teams how to refactor code. Table of Contents 1 Refactoring refactoring 2 Looking under the hood of refactoring PART 1 LEARN BY REFACTORING A COMPUTER GAME 3 Shatter long function 4 Make type codes work 5 Fuse similar code together 6 Defend the data PART 2 TAKING WHAT YOU HAVE LEARNED INTO THE REAL WORLD 7 Collaborate with the compiler 8 Stay away from comments 9 Love deleting code 10 Never be afraid to add code 11 Follow the structure in the code 12 Avoid optimizations and generality 13 Make bad code look bad 14 Wrapping up.