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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.

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

By Aurélien Géron

O Reilly Media
Paperback (568 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

Deep Learning (Adaptive Computation and Machine Learning series)

By Ian Goodfellow & Aaron Courville

The MIT Press
Hardcover (800 pages)

Deep Learning (Adaptive Computation and Machine Learning series)
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  • The MIT Press
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.

Life 3.0: Being Human in the Age of Artificial Intelligence

By Max Tegmark

Knopf
Released: 2017-08-29
Hardcover (384 pages)

Life 3.0: Being Human in the Age of Artificial Intelligence
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New York Times Best Seller

How will Artificial Intelligence affect crime, war, justice, jobs, society and our very sense of being human? The rise of AI has the potential to transform our future more than any other technology—and there’s nobody better qualified or situated to explore that future than Max Tegmark, an MIT professor who’s helped mainstream research on how to keep AI beneficial.

 
How can we grow our prosperity through automation without leaving people lacking income or purpose? What career advice should we give today’s kids? How can we make future AI systems more robust, so that they do what we want without crashing, malfunctioning or getting hacked? Should we fear an arms race in lethal autonomous weapons? Will machines eventually outsmart us at all tasks, replacing humans on the job market and perhaps altogether? Will AI help life flourish like never before or give us more power than we can handle?
 
What sort of future do you want? This book empowers you to join what may be the most important conversation of our time. It doesn’t shy away from the full range of viewpoints or from the most controversial issues—from superintelligence to meaning, consciousness and the ultimate physical limits on life in the cosmos.

AWS Certified Solutions Architect Official Study Guide: Associate Exam

By Joe Baron, Tim Bixler, Kevin E. Kelly & John Stamper

SYBEX
Paperback (437 pages)

AWS Certified Solutions Architect Official Study Guide: Associate Exam
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  • SYBEX
Product Description:

Validate your AWS skills. 

This is your opportunity to take the next step in your career by expanding and validating your skills on the AWS cloud.  AWS has been the frontrunner in cloud computing products and services, and the AWS Certified Solutions Architect Official Study Guide for the Associate exam will get you fully prepared through expert content, and real-world knowledge, key exam essentials, chapter review questions, access to Sybex’s interactive online learning environment, and much more. This official study guide, written by AWS experts, covers exam concepts, and provides key review on exam topics, including:

  • Mapping Multi-Tier Architectures to AWS Services, such as web/app servers, firewalls, caches and load balancers
  • Understanding managed RDBMS through AWS RDS (MySQL, Oracle, SQL Server, Postgres, Aurora)
  • Understanding Loose Coupling and Stateless Systems
  • Comparing Different Consistency Models in AWS Services
  • Understanding how AWS CloudFront can make your application more cost efficient, faster and secure
  • Implementing Route tables, Access Control Lists, Firewalls, NAT, and DNS
  • Applying AWS Security Features along with traditional Information and Application Security
  • Using Compute, Networking, Storage, and Database AWS services
  • Architecting Large Scale Distributed Systems
  • Understanding of Elasticity and Scalability Concepts
  • Understanding of Network Technologies Relating to AWS
  • Deploying and Managing Services with tools such as CloudFormation, OpsWorks and Elastic Beanstalk.

Learn from the AWS subject-matter experts, review with proven study tools, and apply real-world scenarios. If you are looking to take the AWS Certified Solutions Architect Associate exam, this guide is what you need for comprehensive content and robust study tools that will help you gain the edge on exam day and throughout your career. 

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition

By Sebastian Raschka

Packt Publishing - ebooks Account
Released: 2017-09-20
Paperback (622 pages)

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition
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Key Features

  • Second edition of the bestselling book on Machine Learning
  • A practical approach to key frameworks in data science, machine learning, and deep learning
  • Use the most powerful Python libraries to implement machine learning and deep learning
  • Get to know the best practices to improve and optimize your machine learning systems and algorithms

Book Description

Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.

Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library.

Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you'll be ready to meet the new data analysis opportunities in today's world.

If you've read the first edition of this book, you'll be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You'll be able to learn and work with TensorFlow more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn.

What you will learn

  • Understand the key frameworks in data science, machine learning, and deep learning
  • Harness the power of the latest Python open source libraries in machine learning
  • Explore machine learning techniques using challenging real-world data
  • Master deep neural network implementation using the TensorFlow library
  • Learn the mechanics of classification algorithms to implement the best tool for the job
  • Predict continuous target outcomes using regression analysis
  • Uncover hidden patterns and structures in data with clustering
  • Delve deeper into textual and social media data using sentiment analysis

Table of Contents

  1. Giving Computers the Ability to Learn from Data
  2. Training Simple Machine Learning Algorithms for Classification
  3. A Tour of Machine Learning Classifiers Using Scikit-Learn
  4. Building Good Training Sets - Data Preprocessing
  5. Compressing Data via Dimensionality Reduction
  6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning
  7. Combining Different Models for Ensemble Learning
  8. Applying Machine Learning to Sentiment Analysis
  9. Embedding a Machine Learning Model into a Web Application
  10. Predicting Continuous Target Variables with Regression Analysis
  11. Working with Unlabeled Data - Clustering Analysis
  12. Implementing a Multilayer Artificial Neural Network from Scratch
  13. Parallelizing Neural Network Training with TensorFlow
  14. Going Deeper - The Mechanics of TensorFlow
  15. Classifying Images with Deep Convolutional Neural Networks
  16. Modeling Sequential Data using Recurrent Neural Networks

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

By Gareth James & Trevor Hastie

Brand: Springer
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.

Introduction to Algorithms, 3rd Edition (MIT Press)

By Thomas H. Cormen & Ronald L. Rivest

imusti
Hardcover (1312 pages)

Introduction to Algorithms, 3rd Edition (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. As of the third edition, this textbook is published exclusively by the MIT Press.

Bored and Brilliant: How Spacing Out Can Unlock Your Most Productive and Creative Self

By Manoush Zomorodi

St. Martin's Press
Released: 2017-09-05
Hardcover (208 pages)

Bored and Brilliant: How Spacing Out Can Unlock Your Most Productive and Creative Self
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"Bored and Brilliant shows the fascinating side of boredom. Manoush Zomorodi investigates cutting-edge research as well as compelling (and often funny) real-life examples to demonstrate that boredom is actually a crucial tool for making our lives happier, more productive, and more creative. What’s more, the book is crammed with practical exercises for anyone who wants to reclaim the power of spacing out – deleting the Two Dots app, for instance, or having a photo-free day, or taking a 'fakecation'."

Gretchen Rubin, author of #1 NYT Bestseller The Happiness Project

"Bored and Brilliant is full of easy steps to make each day more effective and every life more intentional. Manoush’s mix of personal stories, neuroscience, and data will convince you that boredom is actually a gift."

Charles Duhigg, author of The Power of Habit and Smarter, Faster, Better

It’s time to move “doing nothing” to the top of your to-do list.

In 2015 Manoush Zomorodi, host of WNYC’s popular podcast and radio show Note to Self, led tens of thousands of listeners through an experiment to help them unplug from their devices, get bored, jump-start their creativity, and change their lives. Bored and Brilliant builds on that experiment to show us how to rethink our gadget use to live better and smarter in this new digital ecosystem. Manoush explains the connection between boredom and original thinking, exploring how we can harness boredom’s hidden benefits to become our most productive and creative selves without totally abandoning our gadgets in the process. Grounding the book in the neuroscience and cognitive psychology of “mind wandering” what our brains do when we're doing nothing at all―Manoush includes practical steps you can take to ease the nonstop busyness and enhance your ability to dream, wonder, and gain clarity in your work and life. The outcome is mind-blowing. Unplug and read on.

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

By Trevor Hastie & Jerome Friedman

imusti
Hardcover (745 pages)

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




 
 



   

 
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