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Designing Data-Intensive Applications: The Big Ideas Behind Relia...
by Martin Kleppmann

Language

English

Pages

616

Publication Date

March 16, 2017

Product Description
Customer Reviews
<p>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?</p><p>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.</p><ul><li>Peer under the hood of the systems you already use, and learn how to use and operate them more effectively</li><li>Make informed decisions by identifying the strengths and weaknesses of different tools</li><li>Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity</li><li>Understand the distributed systems research upon which modern databases are built</li><li>Peek behind the scenes of major online services, and learn from their architectures</li></ul>
Database Internals: A Deep Dive into How Distributed Data Systems...
by Alex Petrov

Language

English

Pages

376

Publication Date

September 13, 2019

Product Description
Customer Reviews
<p>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.</p><p>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.</p><p>This book examines:</p><ul><li><b>Storage engines:</b> Explore storage classification and taxonomy, and dive into B-Tree-based and immutable Log Structured storage engines, with differences and use-cases for each</li><li><b>Storage building blocks:</b> Learn how database files are organized to build efficient storage, using auxiliary data structures such as Page Cache, Buffer Pool and Write-Ahead Log</li><li><b>Distributed systems:</b> Learn step-by-step how nodes and processes connect and build complex communication patterns</li><li><b>Database clusters:</b> Which consistency models are commonly used by modern databases and how distributed storage systems achieve consistency</li></ul>
The Hundred-Page Machine Learning Book
by Andriy Burkov

Language

English

Pages

Publication Date

January 12, 2019

Product Description
Customer Reviews
<b>WARNING: will not work on e-ink Kindle devices!</b><br /><br /><b>Peter Norvig</b>, Research Director at Google, co-author of <b>AIMA</b>, the most popular AI textbook in the world: <i>"Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. He succeeds well in choosing the topics — both theory and practice — that will be useful to practitioners, and for the reader who understands that this is the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field."</i><br /><br /><b>Aurélien Géron</b>, Senior AI Engineer, author of the bestseller <b>Hands-On Machine Learning with Scikit-Learn and TensorFlow</b>: <i>"The breadth of topics the book covers is amazing for just 100 pages (plus few bonus pages!). Burkov doesn't hesitate to go into the math equations: that's one thing that short books usually drop. I really liked how the author explains the core concepts in just a few words. The book can be very useful for newcomers in the field, as well as for old-timers who can gain from such a broad view of the field."</i><br /><br /><b>Karolis Urbonas</b>, Head of Data Science at <b>Amazon</b>: <i>"A great introduction to machine learning from a world-class practitioner."</i> <br /><br /><b>Chao Han</b>, VP, Head of R&D at <b>Lucidworks</b>: <i>"I wish such a book existed when I was a statistics graduate student trying to learn about machine learning."</i><br /><br /><b>Sujeet Varakhedi</b>, Head of Engineering at <b>eBay</b>: <i>"Andriy's book does a fantastic job of cutting the noise and hitting the tracks and full speed from the first page.''</i><br /><br /><b>Deepak Agarwal</b>, VP of Artificial Intelligence at <b>LinkedIn</b>: <i>"A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time.''</i><br /><br /><b>Vincent Pollet</b>, Head of Research at <b>Nuance</b>: <i>"The Hundred-Page Machine Learning Book is an excellent read to get started with Machine Learning.''</i><br /><br /><b>Gareth James</b>, Professor of Data Sciences and Operations, co-author of the bestseller <b>An Introduction to Statistical Learning, with Applications in R</b>: <i>"This is a compact “how to do data science” manual and I predict it will become a go-to resource for academics and practitioners alike. At 100 pages (or a little more), the book is short enough to read in a single sitting. Yet, despite its length, it covers all the major machine learning approaches, ranging from classical linear and logistic regression, through to modern support vector machines, deep learning, boosting, and random forests. There is also no shortage of details on the various approaches and the interested reader can gain further information on any particular method via the innovative companion book wiki. The book does not assume any high level mathematical or statistical training or even programming experience, so should be accessible to almost anyone willing to invest the time to learn about these methods. It should certainly be required reading for anyone starting a PhD program in this area and will serve as a useful reference as they progress further. Finally, the book illustrates some of the algorithms using Python code, one of the most popular coding languages for machine learning. I would highly recommend “The Hundred-Page Machine Learning Book” for both the beginner looking to learn more about machine learning and the experienced practitioner seeking to extend their knowledge base."</i><br /><br /><b>Everything you really need to know in Machine Learning in a hundred pages.</b><br /><br />This is the first of its kind <i>"read first, buy later"</i> book. You can find the book online, read it, and then come back to pay for it if you liked the book or found it useful for your work, business or studies.
HBR Guide to Data Analytics Basics for Managers (HBR Guide Series...
by Harvard Business Review

Language

English

Pages

256

Publication Date

March 13, 2018

Product Description
Customer Reviews
<p><b>Don't let a fear of numbers hold you back.</b></p><p>Today's business environment brings with it an onslaught of data. Now more than ever, managers must know how to tease insight from data--to understand where the numbers come from, make sense of them, and use them to inform tough decisions. How do you get started?</p><p>Whether you're working with data experts or running your own tests, you'll find answers in the <b>HBR Guide to Data Analytics Basics for Managers</b>. This book describes three key steps in the data analysis process, so you can get the information you need, study the data, and communicate your findings to others.</p><p>You'll learn how to:</p><ul><li>Identify the metrics you need to measure</li><li>Run experiments and A/B tests</li><li>Ask the right questions of your data experts</li><li>Understand statistical terms and concepts</li><li>Create effective charts and visualizations</li><li>Avoid common mistakes</li></ul><p></p>
Deep Learning Illustrated: A Visual, Interactive Guide to Artific...
by , Aglaé Bassens

Language

English

Pages

416

Publication Date

August 05, 2019

Product Description
Customer Reviews
"The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come."<br />–<i>Tim Urban, author of </i>Wait But Why <b>Fully Practical, Insightful Guide to Modern Deep Learning</b> <br /> <br />Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. <i> <b>Deep Learning Illustrated</b> </i> is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn.<br /> <br />World-class instructor and practitioner Jon Krohn–with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens–presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered.<br /> <br />You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms.<br /> <ul> <li>Discover what makes deep learning systems unique, and the implications for practitioners</li> <li>Explore new tools that make deep learning models easier to build, use, and improve</li> <li>Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more</li> <li>Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects</li> </ul> <i>Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.</i>
The Definitive Guide to DAX: Business intelligence for Microsoft ...
by , Alberto Ferrari

Language

English

Pages

768

Publication Date

July 02, 2019

Product Description
Customer Reviews
<p>Now expanded and updated with modern best practices, this is the most complete guide to Microsoft’s DAX language for business intelligence, data modeling, and analytics. Expert Microsoft BI consultants Marco Russo and Alberto Ferrari help you master everything from table functions through advanced code and model optimization. You’ll learn exactly what happens under the hood when you run a DAX expression, and use this knowledge to write fast, robust code. This edition focuses on examples you can build and run with the free Power BI Desktop, and helps you make the most of the powerful syntax of variables (VAR) in Power BI, Excel, or Analysis Services. Want to leverage all of DAX’s remarkable capabilities? This no-compromise “deep dive” is exactly what you need.</p> <p> <br /> </p> <p>Perform powerful data analysis with DAX for Power BI, SQL Server, and Excel</p> <p>·         Master core DAX concepts, including calculated columns, measures, and calculation groups</p> <p>·         Work efficiently with basic and advanced table functions</p> <p>·         Understand evaluation contexts and the CALCULATE and CALCULATETABLE functions</p> <p>·         Perform time-based calculations</p> <p>·         Use calculation groups and calculation items</p> <p>·         Use syntax of variables (VAR) to write more readable, maintainable code</p> <p>·         Express diverse and unusual relationships with DAX, including many-to-many relationships and bidirectional filters</p> <p>·         Master advanced optimization techniques, and improve performance in aggregations</p> <p>·         Optimize data models to achieve better compression</p> <p> </p> <p>·         Measure DAX query performance with DAX Studio and learn how to optimize your DAX </p>
Data Science from Scratch: First Principles with Python
by Joel Grus

Language

English

Pages

408

Publication Date

April 12, 2019

Product Description
Customer Reviews
<p>To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, and toolkits—but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch.</p><p>If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today’s messy glut of data.</p><ul><li>Get a crash course in Python</li><li>Learn the basics of linear algebra, statistics, and probability—and how and when they’re used in data science</li><li>Collect, explore, clean, munge, and manipulate data</li><li>Dive into the fundamentals of machine learning</li><li>Implement models such as k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering</li><li>Explore recommender systems, natural language processing, network analysis, MapReduce, and databases</li></ul>
OCP Oracle Certified Professional Java SE 11 Programmer I Study G...
by , Scott Selikoff

Language

English

Pages

608

Publication Date

November 19, 2019

Product Description
Customer Reviews
<p><b>The comprehensive study aide for those preparing for the new Oracle Certified Professional Java SE Programmer I Exam 1Z0-815</b></p> <p>Used primarily in mobile and desktop application development, Java is a platform-independent, object-oriented programming language. It is the principal language used in Android application development as well as a popular language for client-side cloud applications. Oracle has updated its Java Programmer certification tracks for Oracle Certified Professional. </p> <p><i>OCP Oracle Certified Professional Java SE 11 Programmer I Study Guide </i>covers 100% of the exam objectives, ensuring that you are thoroughly prepared for this challenging certification exam. This comprehensive, in-depth study guide helps you develop the functional-programming knowledge required to pass the exam and earn certification. All vital topics are covered, including Java building blocks, operators and loops, String and StringBuilder, Array and ArrayList, and more. Included is access to Sybex's superior online interactive learning environment and test bank—containing self-assessment tests, chapter tests, bonus practice exam questions, electronic flashcards, and a searchable glossary of important terms. This indispensable guide:</p> <ul> <li>Clarifies complex material and strengthens your comprehension and retention of key topics</li> <li>Covers all exam objectives such as methods and encapsulation, exceptions, inheriting abstract classes and interfaces, and Java 8 Dates and Lambda Expressions</li> <li>Explains object-oriented design principles and patterns</li> <li>Helps you master the fundamentals of functional programming</li> <li>Enables you to create Java solutions applicable to real-world scenarios</li> </ul> <p>There are over 9 millions developers using Java around the world, yet hiring managers face challenges filling open positions with qualified candidates. The <i>OCP Oracle Certified Professional Java SE 11 Programmer I Study Guide </i>will help you take the next step in your career.</p>
Shale Analytics: Data-Driven Analytics in Unconventional Resource...
by Shahab D. Mohaghegh

Language

English

Pages

287

Publication Date

February 09, 2017

Product Description
Customer Reviews
<p>This book describes the application of modern information technology to reservoir modeling and well management in shale. While covering <b>Shale Analytics</b>, it focuses on reservoir modeling and production management of shale plays, since conventional reservoir and production modeling techniques do not perform well in this environment. Topics covered include tools for analysis, predictive modeling and optimization of production from shale in the presence of massive multi-cluster, multi-stage hydraulic fractures. Given the fact that the physics of storage and fluid flow in shale are not well-understood and well-defined, Shale Analytics avoids making simplifying assumptions and concentrates on facts (Hard Data - Field Measurements) to reach conclusions. Also discussed are important insights into understanding completion practices and re-frac candidate selection and design. The flexibility and power of the technique is demonstrated in numerous real-world situations.</p>
Practical Time Series Analysis: Prediction with Statistics and Ma...
by Aileen Nielsen

Language

English

Pages

505

Publication Date

September 20, 2019

Product Description
Customer Reviews
<p>Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase.</p><p>Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly.</p><p>You’ll get the guidance you need to confidently:</p><ul><li>Find and wrangle time series data</li><li>Undertake exploratory time series data analysis</li><li>Store temporal data</li><li>Simulate time series data</li><li>Generate and select features for a time series</li><li>Measure error</li><li>Forecast and classify time series with machine or deep learning</li><li>Evaluate accuracy and performance</li></ul>

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