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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

Python Crash Course: A Hands-On, Project-Based Introduction to Programming

By Eric Matthes

No Starch Press
Released: 2015-11-01
Paperback (560 pages)

Python Crash Course: A Hands-On, Project-Based Introduction to Programming
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  • No Starch Press
Product Description:
Python Crash Course is a fast-paced, thorough introduction to Python that will have you writing programs, solving problems, and making things that work in no time.

In the first half of the book, you’ll learn about basic programming concepts, such as lists, dictionaries, classes, and loops, and practice writing clean and readable code with exercises for each topic. You’ll also learn how to make your programs interactive and how to test your code safely before adding it to a project. In the second half of the book, you’ll put your new knowledge into practice with three substantial projects: a Space Invaders–inspired arcade game, data visualizations with Python’s super-handy libraries, and a simple web app you can deploy online.

As you work through Python Crash Course you’ll learn how to:
–Use powerful Python libraries and tools, including matplotlib, NumPy, and Pygal
–Make 2D games that respond to keypresses and mouse clicks, and that grow more difficult as the game progresses
–Work with data to generate interactive visualizations
–Create and customize Web apps and deploy them safely online
–Deal with mistakes and errors so you can solve your own programming problems


If you’ve been thinking seriously about digging into programming, Python Crash Course will get you up to speed and have you writing real programs fast. Why wait any longer? Start your engines and code!

Uses Python 2 and 3

Clean Code: A Handbook of Agile Software Craftsmanship

By Robert C. Martin

Prentice Hall
Paperback (464 pages)

Clean Code: A Handbook of Agile Software Craftsmanship
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Even bad code can function. But if code isn’t clean, it can bring a development organization to its knees. Every year, countless hours and significant resources are lost because of poorly written code. But it doesn’t have to be that way.

Noted software expert Robert C. Martin presents a revolutionary paradigm with Clean Code: A Handbook of Agile Software Craftsmanship . Martin has teamed up with his colleagues from Object Mentor to distill their best agile practice of cleaning code “on the fly” into a book that will instill within you the values of a software craftsman and make you a better programmer—but only if you work at it.

What kind of work will you be doing? You’ll be reading code—lots of code. And you will be challenged to think about what’s right about that code, and what’s wrong with it. More importantly, you will be challenged to reassess your professional values and your commitment to your craft.

Clean Code is divided into three parts. The first describes the principles, patterns, and practices of writing clean code. The second part consists of several case studies of increasing complexity. Each case study is an exercise in cleaning up code—of transforming a code base that has some problems into one that is sound and efficient. The third part is the payoff: a single chapter containing a list of heuristics and “smells” gathered while creating the case studies. The result is a knowledge base that describes the way we think when we write, read, and clean code.

Readers will come away from this book understanding
  • How to tell the difference between good and bad code
  • How to write good code and how to transform bad code into good code
  • How to create good names, good functions, good objects, and good classes
  • How to format code for maximum readability
  • How to implement complete error handling without obscuring code logic
  • How to unit test and practice test-driven development
This book is a must for any developer, software engineer, project manager, team lead, or systems analyst with an interest in producing better code.

Deep Learning with Python

By Francois Chollet

Manning Publications
Paperback (384 pages)

Deep Learning with Python
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Summary

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Technology

Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications.

About the Book

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects.

What's Inside

  • Deep learning from first principles
  • Setting up your own deep-learning environment
  • Image-classification models
  • Deep learning for text and sequences
  • Neural style transfer, text generation, and image generation

About the Reader

Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required.

About the Author

François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others.

Table of Contents

    PART 1 - FUNDAMENTALS OF DEEP LEARNING

  1. What is deep learning?
  2. Before we begin: the mathematical building blocks of neural networks
  3. Getting started with neural networks
  4. Fundamentals of machine learning
  5. PART 2 - DEEP LEARNING IN PRACTICE

  6. Deep learning for computer vision
  7. Deep learning for text and sequences
  8. Advanced deep-learning best practices
  9. Generative deep learning
  10. Conclusions
  11. appendix A - Installing Keras and its dependencies on Ubuntu
  12. appendix B - Running Jupyter notebooks on an EC2 GPU instance

Effective Java (3rd Edition)

By Joshua Bloch

Addison-Wesley Professional
Released: 2017-12-27
Paperback (416 pages)

Effective Java (3rd Edition)
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The Definitive Guide to Java Platform Best Practices–Updated for Java 7, 8, and 9

 

Java has changed dramatically since the previous edition of Effective Java was published shortly after the release of Java 6. This Jolt award-winning classic has now been thoroughly updated to take full advantage of the latest language and library features. The support in modern Java for multiple paradigms increases the need for specific best-practices advice, and this book delivers.

 

As in previous editions, each chapter of Effective Java, Third Edition, consists of several “items,” each presented in the form of a short, stand-alone essay that provides specific advice, insight into Java platform subtleties, and updated code examples. The comprehensive descriptions and explanations for each item illuminate what to do, what not to do, and why.

 

The third edition covers language and library features added in Java 7, 8, and 9, including the functional programming constructs that were added to its object-oriented roots. Many new items have been added, including a chapter devoted to lambdas and streams.

 

New coverage includes

  • Functional interfaces, lambda expressions, method references, and streams
  • Default and static methods in interfaces
  • Type inference, including the diamond operator for generic types
  • The @SafeVarargs annotation
  • The try-with-resources statement
  • New library features such as the Optional interface, java.time, and the convenience factory methods for collections


Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

By Wes McKinney

O'Reilly Media
Paperback (544 pages)

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
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Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process.

Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.

  • Use the IPython shell and Jupyter notebook for exploratory computing
  • Learn basic and advanced features in NumPy (Numerical Python)
  • Get started with data analysis tools in the pandas library
  • Use flexible tools to load, clean, transform, merge, and reshape data
  • Create informative visualizations with matplotlib
  • Apply the pandas groupby facility to slice, dice, and summarize datasets
  • Analyze and manipulate regular and irregular time series data
  • Learn how to solve real-world data analysis problems with thorough, detailed examples

Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations

By Nicole Forsgren PhD & Gene Kim

IT Revolution Press
Released: 2018-03-27
Paperback (288 pages)

Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations
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Accelerate your organization to win in the marketplace.

How can we apply technology to drive business value? For years, we've been told that the performance of software delivery teams doesn't matter―that it can't provide a competitive advantage to our companies. Through four years of groundbreaking research to include data collected from the State of DevOps reports conducted with Puppet, Dr. Nicole Forsgren, Jez Humble, and Gene Kim set out to find a way to measure software delivery performance―and what drives it―using rigorous statistical methods. This book presents both the findings and the science behind that research, making the information accessible for readers to apply in their own organizations.

Readers will discover how to measure the performance of their teams, and what capabilities they should invest in to drive higher performance. This book is ideal for management at every level.

Learning Python, 5th Edition

By Mark Lutz

imusti
Paperback (1648 pages)

Learning Python, 5th Edition
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Product Description:

Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Based on author Mark Lutz’s popular training course, this updated fifth edition will help you quickly write efficient, high-quality code with Python. It’s an ideal way to begin, whether you’re new to programming or a professional developer versed in other languages.

Complete with quizzes, exercises, and helpful illustrations, this easy-to-follow, self-paced tutorial gets you started with both Python 2.7 and 3.3— the latest releases in the 3.X and 2.X lines—plus all other releases in common use today. You’ll also learn some advanced language features that recently have become more common in Python code.

  • Explore Python’s major built-in object types such as numbers, lists, and dictionaries
  • Create and process objects with Python statements, and learn Python’s general syntax model
  • Use functions to avoid code redundancy and package code for reuse
  • Organize statements, functions, and other tools into larger components with modules
  • Dive into classes: Python’s object-oriented programming tool for structuring code
  • Write large programs with Python’s exception-handling model and development tools
  • Learn advanced Python tools, including decorators, descriptors, metaclasses, and Unicode processing

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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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.

First Little Readers Parent Pack: Guided Reading Level C: 25 Irresistible Books That Are Just the Right Level for Beginning Readers

By Liza Charlesworth

Scholastic Teaching Resources
Paperback (96 pages)

First Little Readers Parent Pack: Guided Reading Level C: 25 Irresistible Books That Are Just the Right Level for Beginning Readers
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Product Description:
Jumpstart reading success with this big collection motivating storybooks correlated with Guided Reading Level C. Most pages of these full-color storybooks feature just two or three lines of simple, repetitive text to help children learn to read with ease and confidence. Includes a tip-filled parent guide. A great value!
1. The Pie that Jack Made
2. Funny Foods
3. Round the Clown
4. Follow that Cat!
5. Counting Bugs
6. Lunch Crunch
7. Bubble Shapes
8. All About Dinosaurs
9. Make a Pizza
10. This Little Piggy
11. Tail Tale
12. Eight Arms are Great
13. Bat Facts
14. Snow Tracks
15. Lots of Legs
16. Polka-a-Dot World
17. The Teeny Tiny Man
18. Giant Friends
19. Hot Dog, Hot Dog
20. Squares are Everywhere
21. Surprises
22. Shadow Guessing Game
23. Monkey Business
24. Draw a Pig
25. Clay Play For use with Grades PreK-2.



 
 



   

 
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