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Cracking the Coding Interview: 189 Programming Questions and Solutions

By Gayle Laakmann McDowell

CareerCup
Released: 2015-07-01
Paperback (687 pages)

Cracking the Coding Interview: 189 Programming Questions and Solutions
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Product Description:

I am not a recruiter. I am a software engineer. And as such, I know what it's like to be asked to whip up brilliant algorithms on the spot and then write flawless code on a whiteboard. I've been through this as a candidate and as an interviewer.

Cracking the Coding Interview, 6th Edition is here to help you through this process, teaching you what you need to know and enabling you to perform at your very best. I've coached and interviewed hundreds of software engineers. The result is this book.

Learn how to uncover the hints and hidden details in a question, discover how to break down a problem into manageable chunks, develop techniques to unstick yourself when stuck, learn (or re-learn) core computer science concepts, and practice on 189 interview questions and solutions.

These interview questions are real; they are not pulled out of computer science textbooks. They reflect what's truly being asked at the top companies, so that you can be as prepared as possible. WHAT'S INSIDE?

  • 189 programming interview questions, ranging from the basics to the trickiest algorithm problems.
  • A walk-through of how to derive each solution, so that you can learn how to get there yourself.
  • Hints on how to solve each of the 189 questions, just like what you would get in a real interview.
  • Five proven strategies to tackle algorithm questions, so that you can solve questions you haven't seen.
  • Extensive coverage of essential topics, such as big O time, data structures, and core algorithms.
  • A behind the scenes look at how top companies like Google and Facebook hire developers.
  • Techniques to prepare for and ace the soft side of the interview: behavioral questions.
  • For interviewers and companies: details on what makes a good interview question and hiring process.

Illustrations: Illustrations, black and white

 

CompTIA A+ Certification All-in-One Exam Guide, Tenth Edition (Exams 220-1001 & 220-1002)

By Mike Meyers

McGraw-Hill Education
Released: 2019-04-12
Hardcover (1568 pages)

CompTIA A+ Certification All-in-One Exam Guide, Tenth Edition (Exams 220-1001 & 220-1002)
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Product Description:

Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product.

This bestselling on-the-job reference and test preparation guide has been fully revised for the new 2019 CompTIA Core 1 and Core 2 CompTIA A+ exam objectives

This fully revised and updated resource offers complete coverage of the latest release of CompTIA A+ exams 220-1001 & 220-1002. You'll find learning objectives at the beginning of each chapter, exam tips, practice exam questions, and in-depth explanations. Designed to help you pass the CompTIA A+ exams with ease, this definitive guide also serves as an essential on-the-job IT reference.

Also includes a voucher coupon for a 10% discount on your CompTIA exams!

Covers all exam topics, including how to:

Work with CPUs, RAM, BIOS, motherboards, power supplies, and other personal computer components
Install, configure, and maintain hard drives
Manage input devices and removable media
Set up, upgrade, and maintain all versions of Windows
Troubleshoot and fix computer problems
Install printers and other peripherals
Configure and secure mobile devices
Connect to the Internet
Set up wired and wireless networks
Protect your personal computer and your network
Implement virtualization and cloud-based technologies

Online content includes:

Practice exams for 1001 & 1002
More than one hour of free video training
TotalSim simulations of performance-based questions
Mike Meyers’ favorite PC tools and utilities

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

By Aurélien Géron

O'Reilly Media
Released: 2017-04-09
Paperback (574 pages)

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
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  • O Reilly Media
Product Description:

Graphics in this book are printed in black and white.

Through a series of recent 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 practical book shows you how.

By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.

  • Explore the machine learning landscape, particularly neural nets
  • Use scikit-learn to track an example machine-learning project end-to-end
  • Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
  • Use the TensorFlow library to build and train neural nets
  • Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
  • Learn techniques for training and scaling deep neural nets
  • Apply practical code examples without acquiring excessive machine learning theory or algorithm details

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

By Gareth James & Trevor Hastie

Springer
Released: 2017-09-01
Hardcover (426 pages)

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
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Product Description:

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.

Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

Barron's AP Computer Science A with Online Tests

By Roselyn Teukolsky M.S.

Barrons Educational Series
Released: 2018-01-01
Paperback (528 pages)

Barron s AP Computer Science A with Online Tests
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  • Barron s Educational Series Inc U S
Product Description:
Barron’s AP Computer Science A with Online Tests is aligned with the current exam curriculum and provides key practice and subject review. This edition reflects the removal of the ClassCastException and downcasting from the AP Java subset, and practice tests include the free-response style used on recent exams.

This edition includes:
  • Two-full length practice tests in the book
  • Three additional full-length practice tests online
  • A diagnostic test to pinpoint strengths and weaknesses so students can focus on areas where they need more study
  • Answers and explanations for every test question
  • Charts detailing the scoring suggestions for each free-response question

The content review includes static variables, the List interface, enhanced for loops, the import statement, many questions on 2-dimensional arrays, and a detailed analysis of the binary search algorithm.

The Hundred-Page Machine Learning Book

By Andriy Burkov

Andriy Burkov
Paperback (160 pages)

The Hundred-Page Machine Learning Book
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WARNING: to avoid counterfeit, make sure that the book ships from and sold by Amazon. Avoid third-party sellers.

Peter Norvig, Research Director at Google, co-author of AIMA, the most popular AI textbook in the world: "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."

Aurélien Géron, Senior AI Engineer, author of the bestseller Hands-On Machine Learning with Scikit-Learn and TensorFlow: "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."

Karolis Urbonas, Head of Data Science at Amazon: "A great introduction to machine learning from a world-class practitioner."

Chao Han, VP, Head of R&D at Lucidworks: "I wish such a book existed when I was a statistics graduate student trying to learn about machine learning."

Sujeet Varakhedi, Head of Engineering at eBay: "Andriy's book does a fantastic job of cutting the noise and hitting the tracks and full speed from the first page.''

Deepak Agarwal, VP of Artificial Intelligence at LinkedIn: "A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time.''

Vincent Pollet, Head of Research at Nuance: "The Hundred-Page Machine Learning Book is an excellent read to get started with Machine Learning.''

Gareth James, Professor of Data Sciences and Operations, co-author of the bestseller An Introduction to Statistical Learning, with Applications in R: "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."

Everything you really need to know in Machine Learning in a hundred pages.

This is the first of its kind "read first, buy later" 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.

Elements of Programming Interviews in Python: The Insiders' Guide

By Adnan Aziz & Amit Prakash

CreateSpace Independent Publishing Platform
Paperback (458 pages)

Elements of Programming Interviews in Python: The Insiders  Guide
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Take great care to buy the latest version: bit.ly/buyingEPI

EPI includes a framework for writing your solutions, as well as testing them against a comprehensive set of test-cases. Head over to judge.elementsofprogramminginterviews.com for details.

Before you buy this book, please first head over to our sample page - elementsofprogramminginterviews.com/sample

The sampler should give you a very good idea of the quality and style of our book. In particular, be sure you are comfortable with the level and with our Python coding style.

Since different candidates have different time constraints, EPI includes a study guide with several scenarios, ranging from weekend Hackathon to semester long preparation with a recommended a subset of problems for each scenario. 

All problems are classified in terms of their difficulty level and include many variants to help you apply what you have learned more widely.

All problems includes hints for readers who get stuck. This simulates what you will face in the real interview.

The version being sold by Amazon itself is always current. Some resellers may have older versions, especially if they sell used copies.

Introduction to Algorithms, 3rd Edition (The MIT Press)

By Thomas H. Cormen & Ronald L. Rivest

THE MIT
Released: 2009-07-31
Hardcover (1292 pages)

Introduction to Algorithms, 3rd Edition (The MIT Press)
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  • MIT Press MA
Product Description:

The latest edition of the essential text and professional reference, with substantial new material on such topics as vEB trees, multithreaded algorithms, dynamic programming, and edge-based flow.

Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. The book covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers. Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.

The first edition became a widely used text in universities worldwide as well as the standard reference for professionals. The second edition featured new chapters on the role of algorithms, probabilistic analysis and randomized algorithms, and linear programming. The third edition has been revised and updated throughout. It includes two completely new chapters, on van Emde Boas trees and multithreaded algorithms, substantial additions to the chapter on recurrence (now called “Divide-and-Conquer”), and an appendix on matrices. It features improved treatment of dynamic programming and greedy algorithms and a new notion of edge-based flow in the material on flow networks. Many exercises and problems have been added for this edition. The international paperback edition is no longer available; the hardcover is available worldwide.

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

By Trevor Hastie & Jerome Friedman

Springer
Released: 2017-04-21
red Hardcover (745 pages)

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
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  • This refurbished product is tested and certified to work properly. The product will have minor blemishes and/or light scratches. The refurbishing process includes functionality testing, basic cleaning, inspection, and repackaging. The product ships with all relevant accessories, and may arrive in a generic box.
Product Description:

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.

This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates.

The Shallows: What the Internet Is Doing to Our Brains

By Nicholas Carr

W. W. Norton & Company
Paperback (304 pages)

The Shallows: What the Internet Is Doing to Our Brains
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  • Finalist for the Pulitzer Prize
Product Description:

Finalist for the 2011 Pulitzer Prize in General Nonfiction: “Nicholas Carr has written a Silent Spring for the literary mind.”―Michael Agger, Slate

“Is Google making us stupid?” When Nicholas Carr posed that question, in a celebrated Atlantic Monthly cover story, he tapped into a well of anxiety about how the Internet is changing us. He also crystallized one of the most important debates of our time: As we enjoy the Net’s bounties, are we sacrificing our ability to read and think deeply?

Now, Carr expands his argument into the most compelling exploration of the Internet’s intellectual and cultural consequences yet published. As he describes how human thought has been shaped through the centuries by “tools of the mind”―from the alphabet to maps, to the printing press, the clock, and the computer―Carr interweaves a fascinating account of recent discoveries in neuroscience by such pioneers as Michael Merzenich and Eric Kandel. Our brains, the historical and scientific evidence reveals, change in response to our experiences. The technologies we use to find, store, and share information can literally reroute our neural pathways.

Building on the insights of thinkers from Plato to McLuhan, Carr makes a convincing case that every information technology carries an intellectual ethic―a set of assumptions about the nature of knowledge and intelligence. He explains how the printed book served to focus our attention, promoting deep and creative thought. In stark contrast, the Internet encourages the rapid, distracted sampling of small bits of information from many sources. Its ethic is that of the industrialist, an ethic of speed and efficiency, of optimized production and consumption―and now the Net is remaking us in its own image. We are becoming ever more adept at scanning and skimming, but what we are losing is our capacity for concentration, contemplation, and reflection.

Part intellectual history, part popular science, and part cultural criticism, The Shallows sparkles with memorable vignettes―Friedrich Nietzsche wrestling with a typewriter, Sigmund Freud dissecting the brains of sea creatures, Nathaniel Hawthorne contemplating the thunderous approach of a steam locomotive―even as it plumbs profound questions about the state of our modern psyche. This is a book that will forever alter the way we think about media and our minds.



 
 



   

 
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