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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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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.
Number of Pages:696

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 (572 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

The Book of Why: The New Science of Cause and Effect

By Judea Pearl

Basic Books
Released: 2018-05-15
Hardcover (432 pages)

The Book of Why: The New Science of Cause and Effect
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A Turing Award-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize artificial intelligence

"Correlation is not causation." This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality--the study of cause and effect--on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.


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

By Thomas H. Cormen & Ronald L. Rivest

imusti
Hardcover (1312 pages)

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

A new 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 new exercises and problems have been added for this edition. The international paperback edition is no longer available; the hardcover is available worldwide.

Fundamentals of Biochemistry: Life at the Molecular Level

By Donald Voet & Charlotte W. Pratt

Wiley
Hardcover (1152 pages)

Fundamentals of Biochemistry: Life at the Molecular Level
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NOTE: Access Code NOT INCLUDED

Voet, Voet and Pratt’s Fundamentals of Biochemistry, 5th Edition addresses the enormous advances in biochemistry, particularly in the areas of structural biology and Bioinformatics, by providing a solid biochemical foundation that is rooted in chemistry to prepare students for the scientific challenges of the future.  While continuing in its tradition of presenting complete and balanced coverage that is clearly written and relevant to human health and disease, Fundamentals of Biochemistry, 5e includes new pedagogy and enhanced visuals that provide a pathway for student learning.

 

Deep Learning (Adaptive Computation and Machine Learning)

By Ian Goodfellow & Aaron Courville

The MIT Press
Hardcover (775 pages)

Deep Learning (Adaptive Computation and Machine Learning)
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  • deep learning adaptive computation and machine learning series
Product Description:

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.

“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”
―Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.

The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.

Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Algorithms Illuminated (Part 2): Graph Algorithms and Data Structures (Volume 2)

By Tim Roughgarden

Soundlikeyourself Publishing, LLC
Paperback (222 pages)

Algorithms Illuminated (Part 2): Graph Algorithms and Data Structures (Volume 2)
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Algorithms are the heart and soul of computer science. Their applications range from network routing and computational genomics to public-key cryptography and machine learning. Studying algorithms can make you a better programmer, a clearer thinker, and a master of technical interviews. Algorithms Illuminated is an accessible introduction to the subject for anyone with at least a little programming experience. The exposition emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details---like a transcript of what an expert algorithms tutor would say over a series of one-on-one lessons. The book includes solutions to all quizzes and selected problems, and a series of YouTube videos by the author accompanies the book. Part 2 of this book series covers graph search and its applications, shortest-path algorithms, and the applications and implementation of several data structures: heaps, search trees, hash tables, and bloom filters.

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

By Gareth James & Trevor Hastie

Springer
Hardcover (426 pages)

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

Ten Arguments for Deleting Your Social Media Accounts Right Now

By Jaron Lanier

Henry Holt and Co.
Released: 2018-05-29
Hardcover (160 pages)

Ten Arguments for Deleting Your Social Media Accounts Right Now
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A timely call-to-arms from a Silicon Valley pioneer.

You might have trouble imagining life without your social media accounts, but virtual reality pioneer Jaron Lanier insists that we’re better off without them. In Ten Arguments for Deleting Your Social Media Accounts Right Now, Lanier, who participates in no social media, offers powerful and personal reasons for all of us to leave these dangerous online platforms.

Lanier’s reasons for freeing ourselves from social media’s poisonous grip include its tendency to bring out the worst in us, to make politics terrifying, to trick us with illusions of popularity and success, to twist our relationship with the truth, to disconnect us from other people even as we are more “connected” than ever, to rob us of our free will with relentless targeted ads. How can we remain autonomous in a world where we are under continual surveillance and are constantly being prodded by algorithms run by some of the richest corporations in history that have no way of making money other than being paid to manipulate our behavior? How could the benefits of social media possibly outweigh the catastrophic losses to our personal dignity, happiness, and freedom? Lanier remains a tech optimist, so while demonstrating the evil that rules social media business models today, he also envisions a humanistic setting for social networking that can direct us toward a richer and fuller way of living and connecting with our world.

Barron's AP Computer Science A, 8th Edition: with Bonus Online Tests

By Roselyn Teukolsky M.S.

Barron s Educational Series Inc U S
Paperback (528 pages)

Barron s AP Computer Science A, 8th Edition: with Bonus Online Tests
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  • Barron s Educational Series Inc U S
Product Description:
This best-selling guide from Barron's offers practical, proven test-taking strategies and preparation for the Advanced Placement test. This updated manual presents computer science test takers with:
  • Three AP practice tests for the AP Computer Science A test, including a diagnostic test
  • Charts detailing the scoring suggestions for each free-response question
  • Answers and explanations for every test question

A subject 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 book reflects the fact that the ClassCastException and downcasting have been removed from the AP Java subset. The practice exams reflect the new free-response style used on recent AP exams.

BONUS ONLINE PRACTICE TESTS: Students who purchase this book will also get FREE access to three additional full-length online AP Computer Science A tests with all questions answered and explained. These online exams can be easily accessed by smartphone, tablet, or computer.




 
 



   

 
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