-Titulo Original : Database Internals A Deep Dive Into How Distributed Data Systems Work-Fabricante : OReilly Media-Descripcion Original: When it comes to choosing, using, and maintaining a database, understanding its internals is essential. But with so many distributed databases and tools available today, it’s often difficult to understand what each one offers and how they differ. With this practical guide, Alex Petrov guides developers through the concepts behind modern database and storage engine internals.Throughout the book, you’ll explore relevant material gleaned from numerous books, papers, blog posts, and the source code of several open source databases. These resources are listed at the end of parts one and two. You’ll discover that the most significant distinctions among many modern databases reside in subsystems that determine how storage is organized and how data is distributed.This book examines:Storage engines: Explore storage classification and taxonomy, and dive into B-Tree-based and immutable Log Structured storage engines, with differences and use-cases for eachStorage building blocks: Learn how database files are organized to build efficient storage, using auxiliary data structures such as Page Cache, Buffer Pool and Write-Ahead LogDistributed systems: Learn step-by-step how nodes and processes connect and build complex communication patternsDatabase clusters: Which consistency models are commonly used by modern databases and how distributed storage systems achieve consistency About the Author Alex is a data infrastructure engineer, database and storage systems enthusiast, Apache Cassandra committer and PMC member, interested in storage, distributed systems and algorithms...

-Titulo Original : Fluent Python: Clear, Concise, And Effective Programming-Fabricante : OReilly Media-Descripcion Original: Its hard to believe 20 years have transpired since Russ Freeman let it ripp with the formation of the groundbreaking group The Rippingtons, his band that pioneered a new musical format currently known as smooth jazz. Freeman now reunites past and present Ripps for a special 20th anniversary CD release. Guest artists: Dave Koz, Brian McKnight, Patti Austin Jeffrey Osborne Kirk Whalum, Brandon Fields Tony Morales, Gregg Karukas, Paul Taylor Eric Marenthal and more...

-Titulo Original : Building Event-driven Microservices Leveraging Organizational Data At Scale-Fabricante : OReilly Media-Descripcion Original: Organizations today often struggle to balance business requirements with ever-increasing volumes of data. Additionally, the demand for leveraging large-scale, real-time data is growing rapidly among the most competitive digital industries. Conventional system architectures may not be up to the task. With this practical guide, you’ll learn how to leverage large-scale data usage across the business units in your organization using the principles of event-driven microservices.Author Adam Bellemare takes you through the process of building an event-driven microservice-powered organization. You’ll reconsider how data is produced, accessed, and propagated across your organization. Learn powerful yet simple patterns for unlocking the value of this data. Incorporate event-driven design and architectural principles into your own systems. And completely rethink how your organization delivers value by unlocking near-real-time access to data at scale.You’ll learn:How to leverage event-driven architectures to deliver exceptional business valueThe role of microservices in supporting event-driven designsArchitectural patterns to ensure success both within and between teams in your organizationApplication patterns for developing powerful event-driven microservicesComponents and tooling required to get your microservice ecosystem off the ground About the Author Adam Bellemare is a Staff Engineer, Data Platform at Flipp. Hes held this position since 2017. He joined Flipp in 2014 as a senior developer at Flipp. Prior to that, he held positions in embedded software development and quality assurance. His expertise includes: Devops (Kafka, Spark, Mesos, Zookeeper Clusters. Programmatic Building, scaling, destroying); Technical Leadership (Bringing Avro formatting to our data end-to-end, championing Kafka as the event-driven microservice bus, prototyping JRuby, Scala and Java Kafka clients and focusing on removing technical impediments to allow for product delivery); Software Development (Building microservices in Java and Scala using Spark and Kafka libraries); and Data Engineering (Reshaping the way that behavioral data is collected from user devices and shared with our Machine Learning, Billing and Analytics teams)...

-Titulo Original : Javascript The Definitive Guide Master The Worlds Most-used Programming Language-Fabricante : OReilly Media-Descripcion Original: JavaScript is the programming language of the web and is used by more software developers today than any other programming language. For nearly 25 years this best seller has been the go-to guide for JavaScript programmers. The seventh edition is fully updated to cover the 2020 version of JavaScript, and new chapters cover classes, modules, iterators, generators, Promises, async/await, and metaprogramming. You’ll find illuminating and engaging example code throughout. This book is for programmers who want to learn JavaScript and for web developers who want to take their understanding and mastery to the next level. It begins by explaining the JavaScript language itself, in detail, from the bottom up. It then builds on that foundation to cover the web platform and Node.js. Topics include: Types, values, variables, expressions, operators, statements, objects, and arrays Functions, classes, modules, iterators, generators, Promises, and async/await JavaScript’s standard library: data structures, regular expressions, JSON, i18n, etc. The web platform: documents, components, graphics, networking, storage, and threads Node.js: buffers, files, streams, threads, child processes, web clients, and web servers Tools and language extensions that professional JavaScript developers rely on. About the Author David Flanagan is a computer programmer who spends most of his time writing about JavaScript and Java. His books with OReilly include Java in a Nutshell, Java Examples in a Nutshell, Java Foundation Classes in a Nutshell, JavaScript: The Definitive Guide, and JavaScript Pocket Reference. David has a degree in computer science and engineering from the Massachusetts Institute of Technology. He lives with his wife and children in the U.S. Pacific Northwest bewteen the cities of Seattle, Washington and Vancouver, British Columbia...

-Titulo Original : Designing Machine Learning Systems An Iterative Process For Production-ready Applications-Fabricante : OReilly Media-Descripcion Original: Review This is, simply, the very best book you can read about how to build, deploy, and scale machine learning models at a company for maximum impact. Chip is a masterful teacher, and the breadth and depth of her knowledge is unparalleled. - Josh Wills, Software Engineer at WeaveGrid and former Director of Data Engineering, Slack There is so much information one needs to know to be an effective machine learning engineer. Its hard to cut through the chaff to get the most relevant information, but Chip has done that admirably with this book. If you are serious about ML in production, and care about how to design and implement ML systems end to end, this book is essential. - Laurence Moroney, AI and ML Lead, Google One of the best resources that focuses on the first principles behind designing ML systems for production. A must-read to navigate the ephemeral landscape of tooling and platform options. - Goku Mohandas, Founder of Made With ML Chips manual is the book we deserve and the one we need right now. In a blooming but chaotic ecosystem, this principled view on end-to-end ML is both your map and your compass: a must-read for practitioners inside and outside of Big Tech-especially those working at reasonable scale. This book will also appeal to data leaders looking for best practices on how to deploy, manage, and monitor systems in the wild. - Jacopo Tagliabue, Director of AI, Coveo; Adj. Professor of MLSys, NYU Chip is truly a world-class expert on machine learning systems, as well as a brilliant writer. Both are evident in this book, which is a fantastic resource for anyone looking to learn about this topic. - Andrey Kurenkov, PhD Candidate at the Stanford AI Lab Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because theyre data dependent, with data varying wildly from one use case to the next. In this book, youll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references. This book will help you tackle scenarios such as: Engineering data and choosing the right metrics to solve a business problem Automating the process for continually developing, evaluating, deploying, and updating models Developing a monitoring system to quickly detect and address issues your models might encounter in production Architecting an ML platform that serves across use cases Developing responsible ML systems From the Author Ever since the first machine learning course I taught at Stanford in 2017, many people have asked me for advice on how to deploy ML models at their organizations. These questions can be generic, such as What model should I use? How often should I retrain my model? How can I detect data distribution shifts? How do I ensure that the features used during training are consistent with the features used during inference? These questions can also be specific, such as Im convinced that switching from batch prediction to online prediction will give our model a performance boost, but how do I convince my manager to let me do so? or Im the most senior data scientist at my company and Ive recently been tasked with setting up our first machine learning platform; where do I start? My short answer to all these questions is always: It depends. My long answers often involve hours of discussion to understand where the questioner comes from, what theyre actually trying to achieve, and the pros and cons of d...

-Titulo Original : Designing Data-intensive Applications The Big Ideas Behind Reliable, Scalable, And Maintainable Systems-Fabricante : OReilly Media-Descripcion Original: Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords? In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures About the Author Martin is a researcher in distributed systems at the University of Cambridge. Previously he was a software engineer and entrepreneur at Internet companies including LinkedIn and Rapportive, where he worked on large-scale data infrastructure. In the process he learned a few things the hard way, and he hopes this book will save you from repeating the same mistakes. Martin is a regular conference speaker, blogger, and open source contributor. He believes that profound technical ideas should be accessible to everyone, and that deeper understanding will help us develop better software...

-Titulo Original : Software Engineering At Google Lessons Learned From Programming Over Time-Fabricante : OReilly Media-Descripcion Original: Today, software engineers need to know not only how to program effectively but also how to develop proper engineering practices to make their codebase sustainable and healthy. This book emphasizes this difference between programming and software engineering. How can software engineers manage a living codebase that evolves and responds to changing requirements and demands over the length of its life? Based on their experience at Google, software engineers Titus Winters and Hyrum Wright, along with technical writer Tom Manshreck, present a candid and insightful look at how some of the world’s leading practitioners construct and maintain software. This book covers Google’s unique engineering culture, processes, and tools and how these aspects contribute to the effectiveness of an engineering organization. You’ll explore three fundamental principles that software organizations should keep in mind when designing, architecting, writing, and maintaining code: How time affects the sustainability of software and how to make your code resilient over time How scale affects the viability of software practices within an engineering organization What trade-offs a typical engineer needs to make when evaluating design and development decisions. About the Author Titus Winters is a Senior Staff Software Engineer at Google, where he has worked since 2010. Today, he is the chair of the global subcommittee for the design of the C standard library. At Google, he is the library lead for Google’s C codebase: 250 million lines of code that will be edited by 12K distinct engineers in a month. For the last 7 years, Titus and his teams have been organizing, maintaining, and evolving the foundational components of Google’s C codebase using modern automation and tooling. Along the way he has started several Google projects that believed to be in the top 10 largest refactorings in human history. As a direct result of helping to build out refactoring tooling and automation, Titus has encountered first-hand a huge swath of the shortcuts that engineers and programmers may take to “just get something working”. That unique scale and perspective has informed all of his thinking on the care and feeding of software systems. Tom Manshreck is a Staff Technical Writer within Software Engineering at Google since 2005, responsible for developing and maintaining many of Googles core programming guides in infrastructure and language. Since 2011, he has been a member of Googles C Library Team, developing Googles C documentation set, launching (with Titus Winters) Googles C training classes, and documenting Abseil, Googles open source C code. Tom holds a BS in Political Science and a BS in History from the Massachusetts Institute of Technology. Before Google, Tom worked as a Managing Editor at Pearson/Prentice Hall and various startups. Hyrum K. Wright is a Staff Software Engineer at Google, where he has worked since 2012, mainly in the areas of large-scale maintenance of Googles C codebase. Hyrum has made more individual edits to Googles codebase than any other engineer in the history of the company. He is a member of the Apache Software and an occasional visiting faculty member at Carnegie Mellon University. Hyrum received a PhD in Software Engineering from the University of Texas at Austin, and also holds an MS from the University of Texas and a BS from Brigham Young University. He is an active speaker at conferences and contributor to the academic literature on software maintenance and evolution...

-Titulo Original : The Staff Engineers Path A Guide For Individual Contributors Navigating Growth And Change-Fabricante : OReilly Media-Descripcion Original: Review If youre a senior engineer wondering what the next level is, a staff-level engineer or a manager of staff engineers, this book is for you. It covers so many of the things no one tells you about this role-things that take long years, even with great mentors, to discover on your own. -Gergely Orosz, Author of The Pragmatic Engineer Tanya is the perfect author for this exceptional guide to navigating the murky role of staff-plus engineering. Her deep, direct experience comes through in every section. -Will Larson, CTO of Calm, author of Staff Engineer This book feels like the missing manual for my whole career. Its amazingly reassuring to see the ambiguity of the role laid out in print, along with great specific guidance on time management, consensus building, etc. Im going to cite this a lot. -Titus Winters, principal engineer at Google, coauthor of Software Engineering at Google The job of senior leadership as an individual contributor has long been ambiguous and difficult to define, and this book is a much-needed guide on being successful in a relatively new role to our industry. Tanya does an excellent job bringing large-company perspective and scaling company challenges for a rounded view on how to be a successful staff engineer. -Silvia Botros, Coauthor of High Performance MySQL, 4th Edition, and principal engineer The book I wish Id had when I stepped up to Principal Engineer. -Sarah Wells, independent Consultant and Author, former principal engineer at the Financial Times For years, companies have rewarded their most effective engineers with management positions. But treating management as the default path for an engineer with leadership ability doesnt serve the industry well--or the engineer. The staff engineers path allows engineers to contribute at a high level as role models, driving big projects, determining technical strategy, and raising everyones skills. This in-depth book shows you how to understand your role, manage your time, master strategic thinking, and set the standard for technical work. Youll read about how to be a leader without direct authority, how to plan ahead to make the right technical decisions, and how to make everyone around you better, while still growing as an expert in your domain. By exploring the three pillars of a staff engineers job, Tanya Reilly, a veteran of the staff engineer track, shows you how to: Take a broad, strategic view when thinking about your work Dive into practical tactics for making projects succeed Determine what good engineering means in your organization About the Author Tanya Reilly has over twenty years of experience in software engineering, most recently working on architecture and technical strategy as a Senior Principal Engineer at Squarespace. Previously she was a Staff Engineer at Google, responsible for some of the largest distributed systems on the planet. Tanya writes about technical leadership and software reliability at ://noidea.dog. Shes an organizer and host of the LeadDev StaffPlus conference and a frequent conference speaker and keynote. Originally from Ireland, she now lives in Brooklyn with her spouse, kid, and espresso machine...

-Titulo Original : Fundamentals Of Data Engineering Plan And Build Robust Data Systems-Fabricante : OReilly Media-Descripcion Original: Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, youll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle. Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers. Youll understand how to apply the concepts of data generation, ingestion, orchestration, transformation, storage, and governance that are critical in any data environment regardless of the underlying technology. This book will help you: Get a concise overview of the entire data engineering landscape Assess data engineering problems using an end-to-end framework of best practices Cut through marketing hype when choosing data technologies, architecture, and processes Use the data engineering lifecycle to design and build a robust architecture Incorporate data governance and security across the data engineering lifecycle About the Author Joe Reis is a business-minded data nerd whos worked in the data industry for 20 years, with responsibilities ranging from statistical modeling, forecasting, machine learning, data engineering, data architecture, and almost everything else in between. Joe is the CEO and cofounder of Ternary Data, a data engineering and architecture consulting firm based in Salt Lake City, Utah. In addition, he volunteers with several technology groups and teaches at the University of Utah. In his spare time, Joe likes to rock climb, produce electronic music, and take his kids on crazy adventures. Matt Housley is a data engineering consultant and cloud specialist. After some early programming experience with Logo, Basic, and 6502 assembly, he completed a PhD in mathematics at the University of Utah. Matt then began working in data science, eventually specializing in cloud-based data engineering. He cofounded Ternary Data with Joe Reis, where he leverages his teaching experience to train future data engineers and advise teams on robust data architecture. Matt and Joe also pontificate on all things data on The Monday Morning Data Chat...

-Titulo Original : Python For Data Analysis Data Wrangling With Pandas, Numpy, And Jupyter-Fabricante : OReilly Media-Descripcion Original: Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. Youll learn the latest versions of pandas, NumPy, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. Its ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the Jupyter notebook and IPython shell for exploratory computing Learn basic and advanced features in NumPy Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples About the Author Wes McKinney is a Nashville-based software developer and entrepreneur. After finishing his undergraduate degree in mathematics at MIT in 2007, he went on to do quantitative finance work at AQR Capital Management in Greenwich, CT. Frustrated by cumbersome data analysis tools, he learned Python and started building what would later become the pandas project. Hes now an active member of the Python data community and is an advocate for the use of Python in data analysis, finance, and statistical computing applications. Wes was later the cofounder and CEO of DataPad, whose technology assets and team were acquired by Cloudera in 2014. He has since become involved in big data technology, joining the Project Management Committees for the Apache Arrow and Apache Parquet projects in the Apache Software Foundation. In 2018, he founded Ursa Labs, a not-for-profit organization focused Apache Arrow development, in partnership with RStudio and Two Sigma Investments. In 2021, he cofounded technology startup Voltron Data, where he currently works as the Chief Technology Officer...

-Titulo Original : Generative Deep Learning Teaching Machines To Paint, Write, Compose, And Play-Fabricante : OReilly Media-Descripcion Original: Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to create impressive generative deep learning models from scratch using Tensorflow and Keras, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy-based models, and denoising diffusion models. The book starts with the basics of deep learning and progresses to cutting-edge architectures. Through tips and tricks, readers can make their models learn more efficiently and become more creative. Discover how VAEs can change facial expressions in photos Train GANs to generate images based on your own dataset Build diffusion models to produce new varieties of flowers Train your own GPT for text generation Learn how large language models like ChatGPT are trained Explore state-of-the-art architectures such as StyleGAN 2 and Vision Transformer VQ-GAN Compose polyphonic music using Transformers and MuseGAN Understand how generative world models can solve reinforcement learning tasks Dive into multimodal models such as DALL.E 2, Imagen and Stable Diffusion for text-to-image generation The book also explores the future of generative AI and how individuals and companies can proactively begin to leverage this remarkable new technology to create competitive advantage...

-Titulo Original : R For Data Science Import, Tidy, Transform, Visualize, And Model Data-Fabricante : OReilly Media-Descripcion Original: Use R to turn data into insight, knowledge, and understanding. With this practical book, aspiring data scientists will learn how to do data science with R and RStudio, along with the tidyversea??a collection of R packages designed to work together to make data science fast, fluent, and fun. Even if you have no programming experience, this updated edition will have you doing data science quickly. Youll learn how to import, transform, and visualize your data and communicate the results. And youll get a complete, big-picture understanding of the data science cycle and the basic tools you need to manage the details. Updated for the latest tidyverse features and best practices, new chapters show you how to get data from spreadsheets, databases, and websites. Exercises help you practice what youve learned along the way. Youll understand how to: Visualize: Create plots for data exploration and communication of results Transform: Discover variable types and the tools to work with them Import: Get data into R and in a form convenient for analysis Program: Learn R tools for solving data problems with greater clarity and ease Communicate: Integrate prose, code, and results with Quart...

-Titulo Original : Natural Language Processing With Transformers, Revised Edition-Fabricante : OReilly Media-Descripcion Original: Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks. If youre a data scientist or coder, this practical book -now revised in full color- shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library. Transformers have been used to write realistic news stories, improve Google Search queries, and even create chatbots that tell corny jokes. In this guide, authors Lewis Tunstall, Leandro von Werra, and Thomas Wolf, among the creators of Hugging Face Transformers, use a hands-on approach to teach you how transformers work and how to integrate them in your applications. Youll quickly learn a variety of tasks they can help you solve. Build, debug, and optimize transformer models for core NLP tasks, such as text classification, named entity recognition, and question answering Learn how transformers can be used for cross-lingual transfer learning Apply transformers in real-world scenarios where labeled data is scarce Make transformer models efficient for deployment using techniques such as distillation, pruning, and quantization Train transformers from scratch and learn how to scale to multiple GPUs and distributed environment...

-Titulo Original : Laws Of Ux Using Psychology To Design Better Products & Services-Fabricante : OReilly Media-Descripcion Original: An understanding of psychology-specifically the psychology behind how users behave and interact with digital interfaces-is perhaps the single most valuable nondesign skill a designer can have. The most elegant design can fail if it forces users to conform to the design rather than working within the blueprint of how humans perceive and process the world around them. This practical guide explains how you can apply key principles in psychology to build products and experiences that are more intuitive and human-centered. Author Jon Yablonski deconstructs familiar apps and experiences to provide clear examples of how UX designers can build experiences that adapt to how users perceive and process digital interfaces. You’ll learn: How aesthetically pleasing design creates positive responses The principles from psychology most useful for designers How these psychology principles relate to UX heuristics Predictive models including Fitts’s law, Jakob’s law, and Hick’s law Ethical implications of using psychology in design A framework for applying these principles ...

-Titulo Original : Hands-on Machine Learning With Scikit-learn, Keras, And Tensorflow Concepts, Tools, And Techniques To Build Intelligent Systems-Fabricante : OReilly Media-Descripcion Original: Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. With this updated third edition, author Aurelien Geron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what youve learned. Programming experience is all you need to get started. Use Scikit-learn to track an example ML project end to end Explore several models, including support vector machines, decision trees, random forests, and ensemble methods Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learnin...

-Titulo Original : Essential Math For Data Science Take Control Of Your Data With Fundamental Linear Algebra, Probability, And Statistics-Fabricante : OReilly Media-Descripcion Original: Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way youll also gain practical insights into the state of data science and how to use those insights to maximize your career. Learn how to: Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance Manipulate vectors and matrices and perform matrix decomposition Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job marke...

-Titulo Original : Tidy First? A Personal Exercise In Empirical Software Design-Fabricante : OReilly Media-Descripcion Original: Messy code is a nuisance. Tidying code, to make it more readable, requires breaking it up into manageable sections. In this practical guide, author Kent Beck, creator of Extreme Programming and pioneer of software patterns, suggests when and where you might apply tidyings to improve your code while keeping the overall structure of the system in mind. Instead of trying to master tidying all at once, this book lets you try out a few examples that make sense for your problem. If you have a big function containing many lines of code, youll learn how to logically divide it into smaller chunks. Along the way, youll learn the theory behind software design: coupling, cohesion, discounted cash flows, and optionality. This book helps you: Understand the basic theory of how software design works and the forces that act on it Explore the difference between changes to a systems behavior and changes to its structure Improve your programming experience by sometimes tidying first and sometimes tidying after Learn how to make large changes in small, safe steps Approach software design as an exercise in human relationship...

-Titulo Original : Java Performance: In-Depth Advice For Tuning And Programming Java 8, 11, And Beyond-Fabricante : OReilly Media-Descripcion Original: Coding and testing are generally considered separate areas of expertise. In this practical book, Java expert Scott Oaks takes the approach that anyone who works with Java should be adept at understanding how code behaves in the Java Virtual Machine?including the tunings likely to help performance. This updated second edition helps you gain in-depth knowledge of Java application performance using both the JVM and the Java platform. Developers and performance engineers alike will learn a variety of features, tools, and processes for improving the way the Java 8 and 11 LTS releases perform. While the emphasis is on production-supported releases and features, this book also features previews of exciting new technologies such as ahead-of-time compilation and experimental garbage collections. Understand how various Java platforms and compilers affect performance Learn how Java garbage collection works Apply four principles to obtain best results from performance testing Use the JDK and other tools to learn how a Java application is performing Minimize the garbage collector’s impact through tuning and programming practices Tackle performance issues in Java APIs Improve Java-driven database application performanc...

-Titulo Original : Optimizing Java: Practical Techniques For Improving JVM Application Performance-Fabricante : OReilly Media-Descripcion Original: Performance tuning is an experimental science, but that doesnt mean engineers should resort to guesswork and folklore to get the job done. Yet thats often the case. With this practical book, intermediate to advanced Java technologists working with complex platforms will learn how to tune Java cloud applications for performance using a quantitative, verifiable, and repeatable approach. In response to the ubiquity of cloud computing, this revised edition addresses topics that are key to high performance of Java applications in the cloud. Many resources on performance tend to focus on the theory and internals of Java virtual machines, but this book discusses the low-level technical aspects within the context of performance-tuning practicalities and examines a wide range of aspects. There are no simple recipes, tips and tricks, or algorithms to learn. Instead, performance analysis is a process of defining and determining desired outcomes and using in-depth technical knowledge to inform how to reach those goals. With this book, you will: Learn how Java principles and technology make the best use of modern hardware, operating systems, and cloud stacks Examine the pitfalls of measuring Java performance numbers and the drawbacks of microbenchmarking Understand how to package, deploy, operate, and debug Java/JVM applications in modern cloud environments Apply emerging observability approaches to obtain deep understanding of cloud native applications Use Java language performance techniques including concurrent and distributed form...

-Titulo Original : Head First Java: A Brain-Friendly Guide-Fabricante : OReilly Media-Descripcion Original: What will you learn from this book? Head First Java is a complete learning experience in Java and object-oriented programming. With this book, youll learn the Java language with a unique method that goes beyond how-to manuals and helps you become a great programmer. Through puzzles, mysteries, and soul-searching interviews with famous Java objects, youll quickly get up to speed on Javas fundamentals and advanced topics including lambdas, streams, generics, threading, networking, and the dreaded desktop GUI. If you have experience with another programming language, Head First Java will engage your brain with more modern approaches to coding--the sleeker, faster, and easier to read, write, and maintain Java of today. Whats so special about this book? If youve read a Head First book, you know what to expect--a visually rich format designed for the way your brain works. If you havent, youre in for a treat. With Head First Java, youll learn Java through a multisensory experience that engages your mind, rather than by means of a text-heavy approach that puts you to sleep...

-Titulo Original : Head First Design Patterns: Building Extensible And Maintainable Object-Oriented Software 2nd Edition-Fabricante : OReilly Media-Descripcion Original: What will you learn from this book? You know you dont want to reinvent the wheel, so you look to Design Patterns: the lessons learned by those whove faced the same software design problems. With Design Patterns, you get to take advantage of the best practices and experience of others so you can spend your time on something more challenging. Something more fun. This book shows you the patterns that matter, when to use them and why, how to apply them to your own designs, and the object-oriented design principles on which theyre based. Join hundreds of thousands of developers whove improved their object-oriented design skills through Head First Design Patterns. Whats so special about this book? If youve read a Head First book, you know what to expect: a visually rich format designed for the way your brain works. With Head First Design Patterns, 2E youll learn design principles and patterns in a way that wont put you to sleep, so you can get out there to solve software design problems and speak the language of patterns with others on your team...

-Titulo Original : Programming Rust: Fast, Safe Systems Development-Fabricante : OReilly Media-Descripcion Original: Systems programming provides the foundation for the worlds computation. Writing performance-sensitive code requires a programming language that puts programmers in control of how memory, processor time, and other system resources are used. The Rust systems programming language combines that control with a modern type system that catches broad classes of common mistakes, from memory management errors to data races between threads. With this practical guide, experienced systems programmers will learn how to successfully bridge the gap between performance and safety using Rust. Jim Blandy, Jason Orendorff, and Leonora Tindall demonstrate how Rusts features put programmers in control over memory consumption and processor use by combining predictable performance with memory safety and trustworthy concurrency. Youll learn: Rusts fundamental data types and the core concepts of ownership and borrowing How to write flexible, efficient code with traits and generics How to write fast, multithreaded code without data races Rusts key power tools: closures, iterators, and asynchronous programming Collections, strings and text, input and output, macros, unsafe code, and foreign function interfaces This revised, updated edition covers the Rust 2021 Edition...

-Titulo Original : Prompt Engineering For Generative Ai Future-proof Inputs For Reliable Ai Outputs-Fabricante : OReilly Media-Descripcion Original: Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential. Because they have been trained on all the public text and images on the internet, they can make useful contributions to a wide variety of tasks. And with the barrier to entry greatly reduced today, practically any developer can harness LLMs and diffusion models to tackle problems previously unsuitable for automation. With this book, youll gain a solid foundation in generative AI, including how to apply these models in practice. When first integrating LLMs and diffusion models into their workflows, most developers struggle to coax reliable enough results from them to use in automated systems. Authors James Phoenix and Mike Taylor show you how a set of principles called prompt engineering can enable you to work effectively with AI. Learn how to empower AI to work for you. This book explains: The structure of the interaction chain of your programs AI model and the fine-grained steps in between How AI model requests arise from transforming the application problem into a document completion problem in the model training domain The influence of LLM and diffusion model architecture-and how to best interact with it How these principles apply in practice in the domains of natural language processing, text and image generation, and cod...

-Titulo Original : Leading Effective Engineering Teams Lessons For Individual Contributors And Managers From 10 Years At Google-Fabricante : OReilly Media-Descripcion Original: In this insightful and comprehensive guide, Addy Osmani shares more than a decade of experience working on the Chrome team at Google, uncovering secrets to engineering effectiveness, efficiency, and team success. Engineers and engineering leaders looking to scale their effectiveness and drive transformative results within their teams and organizations will learn the essential principles, tips, and frameworks for building highly effective engineering teams. Osmani presents best practices and proven strategies that foster engineering excellence in organizations of all sizes. Through practical advice and real-world examples, Leading Effective Engineering Teams empowers you to create a thriving engineering culture where individuals and teams can excel. Unlock the full potential of your engineering team and achieve unparalleled success by harnessing the power of trust, commitment, and accountability. With this book, youll discover: The essential traits for engineering effectiveness and the pitfalls to avoid How to cultivate trust, commitment, and accountability within your team Strategies to minimize friction, optimize career growth, and deliver maximum value The dynamics of highly successful engineering teams and how to replicate their achievements How to implement a systems thinking approach for everyday problem-solving and decision-making Self-advocacy techniques to enhance your teams visibility and recognition within the organizatio...