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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>
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>
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>
Targeted: The Cambridge Analytica Whistleblower's Inside Story of...
by Brittany Kaiser

Language

English

Pages

404

Publication Date

October 22, 2019

Product Description
Customer Reviews
<p><strong>In this explosive memoir, a political consultant and technology whistleblower reveals the disturbing truth about the multi-billion-dollar data industry, revealing to the public how companies are getting richer using our personal information and exposing how Cambridge Analytica exploited weaknesses in privacy laws to help elect Donald Trump—and how this could easily happen again in the 2020 presidential election.<br /></strong><br />When Brittany Kaiser joined Cambridge Analytica—the UK-based political consulting firm funded by conservative billionaire and Donald Trump patron Robert Mercer—she was an idealistic young professional working on her fourth degree in human rights law and international relations. A veteran of Barack Obama’s 2008 campaign, Kaiser’s goal was to utilize data for humanitarian purposes, most notably to prevent genocide and human rights abuses. But her experience inside Cambridge Analytica opened her eyes to the tremendous risks that this unregulated industry poses to privacy and democracy.</p><p></p><p><em>Targeted</em> is Kaiser’s eyewitness chronicle of the dramatic and disturbing story of the rise and fall of Cambridge Analytica. She reveals to the public how Facebook’s lax policies and lack of sufficient national laws allowed voters to be manipulated in both Britain and the United States, where personal data was weaponized to spread fake news and racist messaging during the Brexit vote and the 2016 election. But the damage isn’t done Kaiser warns; the 2020 election can be compromised as well if we continue to do nothing.</p><p></p><p>In the aftermath of the U.S. election, as she became aware of the horrifying reality of what Cambridge Analytica had done in support of Donald Trump, Kaiser made the difficult choice to expose the truth. Risking her career, relationships, and personal safety, she told authorities about the data industry’s unethical business practices, eventually testifying before Parliament about the company’s Brexit efforts and helping Special Counsel Robert Mueller’s investigation into Russian interference in the 2016 election, alongside at least 10 other international investigations.</p><p></p><p>Packed with never-before-publicly-told stories and insights, <em>Targeted</em> goes inside the secretive meetings with Trump campaign personnel and details the promises Cambridge Analytica made to win. Throughout, Kaiser makes the case for regulation, arguing that legal oversight of the data industry is not only justifiable but essential to ensuring the long-term safety of our democracy.</p><p></p><p><em>Targeted</em> includes 20-30 photos.</p>
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.
Data Science for Business: What You Need to Know about Data Minin...
by , Tom Fawcett

Language

English

Pages

414

Publication Date

July 27, 2013

Product Description
Customer Reviews
<p>Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.</p><p>Based on an MBA course Provost has taught at New York University over the past ten years, <i>Data Science for Business</i> provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.</p><ul><li>Understand how data science fits in your organization—and how you can use it for competitive advantage</li><li>Treat data as a business asset that requires careful investment if you’re to gain real value</li><li>Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way</li><li>Learn general concepts for actually extracting knowledge from data</li><li>Apply data science principles when interviewing data science job candidates</li></ul>
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>
Database System Concepts
by Abraham Silberschatz

Language

English

Pages

1376

Publication Date

February 19, 2019

Product Description
Customer Reviews
Database System Concepts by Silberschatz Korth and Sudarshan is now in its 6th edition and is one of the cornerstone texts of database education. It presents the fundamental concepts of database management in an intuitive manner geared toward allowing students to begin working with databases as quickly as possible.The text is designed for a first course in databases at the junior/senior undergraduate level or the first year graduate level. It also contains additional material that can be used as supplements or as introductory material for an advanced course. Because the authors present concepts as intuitive descriptions a familiarity with basic data structures computer organization and a high-level programming language are the only prerequisites. Important theoretical results are covered but formal proofs are omitted. In place of proofs figures and examples are used to suggest why a result is true.
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>
SQL in 10 Minutes, Sams Teach Yourself: Sams Teac Your SQL 10 Min...
by Ben Forta

Language

English

Pages

287

Publication Date

October 25, 2012

Product Description
Customer Reviews
Sams Teach Yourself SQL in 10 Minutes, Fourth Edition New full-color code examples help you see how SQL statements are structured Whether you're an application developer, database administrator, web application designer, mobile app developer, or Microsoft Office users, a good working knowledge of SQL is an important part of interacting with databases. And Sams Teach Yourself SQL in 10 Minutes offers the straightforward, practical answers you need to help you do your job. Expert trainer and popular author Ben Forta teaches you just the parts of SQL you need to know–starting with simple data retrieval and quickly going on to more complex topics including the use of joins, subqueries, stored procedures, cursors, triggers, and table constraints. You'll learn methodically, systematically, and simply–in 22 short, quick lessons that will each take only 10 minutes or less to complete. With the Fourth Edition of this worldwide bestseller, the book has been thoroughly updated, expanded, and improved. Lessons now cover the latest versions of IBM DB2, Microsoft Access, Microsoft SQL Server, MySQL, Oracle, PostgreSQL, SQLite, MariaDB, and Apache Open Office Base. And new full-color SQL code listings help the beginner clearly see the elements and structure of the language. 10 minutes is all you need to learn how to... Use the major SQL statements Construct complex SQL statements using multiple clauses and operators Retrieve, sort, and format database contents Pinpoint the data you need using a variety of filtering techniques Use aggregate functions to summarize data Join two or more related tables Insert, update, and delete data Create and alter database tables Work with views, stored procedures, and more Table of Contents 1 Understanding SQL 2 Retrieving Data 3 Sorting Retrieved Data 4 Filtering Data 5 Advanced Data Filtering 6 Using Wildcard Filtering 7 Creating Calculated Fields 8 Using Data Manipulation Functions 9 Summarizing Data 10 Grouping Data 11 Working with Subqueries 12 Joining Tables 13 Creating Advanced Joins 14 Combining Queries 15 Inserting Data 16 Updating and Deleting Data 17 Creating and Manipulating Tables 18 Using Views 19 Working with Stored Procedures 20 Managing Transaction Processing 21 Using Cursors 22 Understanding Advanced SQL Features Appendix A: Sample Table Scripts Appendix B: Working in Popular Applications Appendix C : SQL Statement Syntax Appendix D: Using SQL Datatypes Appendix E: SQL Reserved Words

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