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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鈥檚 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鈥檒l 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鈥檒l 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 鈥?both theory and practice 鈥?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>Aur茅lien G茅ron</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 鈥渉ow to do data science鈥?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 鈥淭he Hundred-Page Machine Learning Book鈥?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.
Deep Learning Illustrated: A Visual, Interactive Guide to Artific...
by , Agla茅 Bassens

Language

English

Pages

416

Publication Date

August 05, 2019

Product Description
Customer Reviews
"The authors鈥?clear visual style provides a comprehensive look at what鈥檚 currently possible with artificial neural networks as well as a glimpse of the magic that鈥檚 to come."<br />鈥?lt;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鈥檚 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鈥搘ith visionary content from Grant Beyleveld and beautiful illustrations by Agla茅 Bassens鈥損resents 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鈥檒l 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鈥檚 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鈥檒l 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鈥檚 remarkable capabilities? This no-compromise 鈥渄eep dive鈥?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>路聽聽聽聽聽聽聽聽 Master core DAX concepts, including calculated columns, measures, and calculation groups</p> <p>路聽聽聽聽聽聽聽聽 Work efficiently with basic and advanced table functions</p> <p>路聽聽聽聽聽聽聽聽 Understand evaluation contexts and the CALCULATE and CALCULATETABLE functions</p> <p>路聽聽聽聽聽聽聽聽 Perform time-based calculations</p> <p>路聽聽聽聽聽聽聽聽 Use calculation groups and calculation items</p> <p>路聽聽聽聽聽聽聽聽 Use syntax of variables (VAR) to write more readable, maintainable code</p> <p>路聽聽聽聽聽聽聽聽 Express diverse and unusual relationships with DAX, including many-to-many relationships and bidirectional filters</p> <p>路聽聽聽聽聽聽聽聽 Master advanced optimization techniques, and improve performance in aggregations</p> <p>路聽聽聽聽聽聽聽聽 Optimize data models to achieve better compression</p> <p> </p> <p>路聽聽聽聽聽聽聽聽 Measure DAX query performance with DAX Studio and learn how to optimize your DAX聽</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鈥攄ata science libraries, frameworks, modules, and toolkits鈥攂ut 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鈥檚 messy glut of data.</p><ul><li>Get a crash course in Python</li><li>Learn the basics of linear algebra, statistics, and probability鈥攁nd how and when they鈥檙e 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, Na茂ve 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>
Cuisinart Air Fryer Oven Cookbook for Beginners: Easy & Delicious...
by Isabell Kerr

Language

English

Pages

174

Publication Date

December 24, 2019

Product Description
Customer Reviews
<h2><strong>Cuisinart Air Fryer Oven Cookbook for Beginners: Enjoy Easy & Delicious recipes for your Cuisinart Air Fryer!</strong></h2><p><strong></strong><br /></p><p><strong></strong>We bring a collection of some of the best recipes for air fried food plus the right method for their preparation!<br /></p><h2><strong>Here鈥檚 what you鈥檒l find:</strong></h2><ul><li><p><strong>Mouth-watering sandwich ideas for your lunchbox</strong></p></li><li><p>Protein-rich and low-fat omelets 鈥?a must if you鈥檙e physically active!</p></li><li><p><strong>Fast, delicious, and healthy breakfast ideas to start your day</strong></p></li><li><p>Dozens of vegetarian and vegan recipes that will conquer your heart even if you normally eat steaks every day</p></li><li><p>Beef, Pork and Lamb Recipes</p></li><li><p><strong>Snacks, desserts, and much, much more!</strong><strong></strong></p></li></ul><p><em><u><strong></strong></u></em><br /></p><p><strong></strong><em><u><strong>Are you ready to discover the potential of your air fryer?</strong></u></em></p><h2>Scroll up, click on聽<strong>"Buy Now with 1-Click"</strong>, and Get Your Copy Now!</h2><strong></strong><strong></strong>
VMware vSAN 6.7 U1 Deep Dive
by , Duncan Epping

Language

English

Pages

404

Publication Date

December 09, 2018

Product Description
Customer Reviews
VMware鈥檚 vSAN has rapidly proven itself in environments ranging from hospitals to oil rigs to e-commerce platforms and is one of the top three players in the hyperconverged space. Along the way, it has matured to offer unsurpassed features for data integrity, availability, and space efficiency. vSAN 6.7 U1 has radically simplified IT operations and supports the transition to hyperconverged infrastructures (HCI). <br /><br />The authors of the vSAN Deep Dive have thoroughly updated their definitive guide to this transformative technology. Writing for vSphere administrators, architects, and consultants, Cormac Hogan, and Duncan Epping explain what vSAN is, how it has evolved, what it now offers, and how to gain maximum value from it. The book offers expert insight into preparation, installation, configuration, policies, provisioning, clusters, architecture, and more. You鈥檒l also find practical guidance for using all data services, stretched clusters, and two node configurations. This book is part of the Deep Dive series.<br /><br />Combine this book with the vSphere 6.5 Host Deep Dive, and the vSphere 6.7 Clustering Deep Dive, and you have an in-depth and comprehensive set of books that deliver the information you need to design and administer both vSphere and vSAN in the enterprise.<br /><br />Often referred to in the virtual community as the vSphere Resource kit, the Host Resource Deep Dive zooms in on hardware resources such as CPU and Memory and covers how the vSphere resource scheduler manages these. The vSAN Deep Dive discusses how to leverage local storage devices to create a shared storage platform and the Clustering Deep Dive builds on top of that and zooms in how a group of ESXi hosts work together and provide compute clustering services. Buy all three and become your organizations' private cloud superhero!
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聽<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.聽The flexibility and power of the technique is demonstrated in numerous real-world situations.</p>
New Developments in Classification and Data Analysis: Proceedings...
by Springer

Language

English

Pages

370

Publication Date

March 30, 2006

Product Description
Customer Reviews
This volume contains revised versions of selected papers presented during the biannual meeting of the Classification and Data Analysis Group of SocietA Italiana di Statistica, which was held in Bologna, September 22-24, 2003. The scientific program of the conference included 80 contributed papers. Moreover it was possible to recruit six internationally renowned invited spe- ers for plenary talks on their current research works regarding the core topics of IFCS (the International Federation of Classification Societies) and Wo- gang Gaul and the colleagues of the GfKl organized a session. Thus, the conference provided a large number of scientists and experts from home and abroad with an attractive forum for discussions and mutual exchange of knowledge. The talks in the different sessions focused on methodological developments in supervised and unsupervised classification and in data analysis, also p- viding relevant contributions in the context of applications. This suggested the presentation of the 43 selected papers in three parts as follows: CLASSIFICATION AND CLUSTERING Non parametric classification Clustering and dissimilarities MULTIVARIATE STATISTICS AND DATA ANALYSIS APPLIED MULTIVARIATE STATISTICS Environmental data Microarray data Behavioural and text data Financial data We wish to express our gratitude to the authors whose enthusiastic p- ticipation made the meeting possible. We are very grateful to the reviewers for the time spent in their professional reviewing work. We would also like to extend our thanks to the chairpersons and discussants of the sessions: their comments and suggestions proved very stimulating both for the authors and the audience.

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