And it’s still short, profusely illustrated…and best of all–fun to read.If you’ve read it before, you’ll rediscover what made Don’t Make Me Think so essential to Web designers and developers around the world. If you’ve never read it, you’ll see why so many people have said it should be required reading for anyone working on Web sites.

    This is a deeply technical book and focuses on the software engineering skills to ace your interview. The book is over 500 pages and includes 150 programming interview questions and answers, as well as other advice.The full list of topics are as follows:The Interview ProcessThis section offers an overview on questions are selected and how you will be evaluated. What happens when you get a question wrong? When should you start preparing, and how? What language should you use? All these questions and more are answered.Behind the ScenesLearn what happens behind the scenes during your interview, how decisions really get made, who you interview with, and what they ask you. Companies covered include Google, Amazon, Yahoo, Microsoft, Apple and Facebook.Special SituationsThis section explains the process for experience candidates, Program Managers, Dev Managers, Testers / SDETs, and more. Learn what your interviewers are looking for and how much code you need to know.Before the InterviewIn order to ace the interview, you first need to get an interview. This section describes what a software engineer's resume should look like and what you should be doing well before your interview.Behavioral PreparationAlthough most of a software engineering interview will be technical, behavioral questions matter too. This section covers how to prepare for behavioral questions and how to give strong, structured responses.Technical Questions (+ 5 Algorithm Approaches)This section covers how to prepare for technical questions (without wasting your time) and teaches actionable ways to solve the trickiest algorithm problems. It also teaches you what exactly "good coding" is when it comes to an interview.150 Programming Questions and AnswersThis section forms the bulk of the book. Each section opens with a discussion of the core knowledge and strategies to tackle this type of question, diving into exactly how you break down and solve it. Topics covered include• Arrays and Strings• Linked Lists• Stacks and Queues• Trees and Graphs• Bit Manipulation• Brain Teasers• Mathematics and Probability• Object-Oriented Design• Recursion and Dynamic Programming• Sorting and Searching• Scalability and Memory Limits• Testing• C and C++• Java• Databases• Threads and LocksFor the widest degree of readability, the solutions are almost entirely written with Java (with the exception of C / C++ questions). A link is provided with the book so that you can download, compile, and play with the solutions yourself.Changes from the Fourth Edition: The fifth edition includes over 200 pages of new content, bringing the book from 300 pages to over 500 pages. Major revisions were done to almost every solution, including a number of alternate solutions added. The introductory chapters were massively expanded, as were the opening of each of the chapters under Technical Questions. In addition, 24 new questions were added.Cracking the Coding Interview, Fifth Edition is the most expansive, detailed guide on how to ace your software development / programming interviews.

    Mitnick claims that this socialengineering tactic was the single-most effective method in hisarsenal. This indispensable book examines a variety of maneuversthat are aimed at deceiving unsuspecting victims, while it alsoaddresses ways to prevent social engineering threats.Examines social engineering, the science of influencing atarget to perform a desired task or divulge informationArms you with invaluable information about the many methods oftrickery that hackers use in order to gather information with theintent of executing identity theft, fraud, or gaining computersystem accessReveals vital steps for preventing social engineeringthreatsSocial Engineering: The Art of Human Hacking does itspart to prepare you against nefarious hackers--now you can doyour part by putting to good use the critical information withinits pages.

    The R language is recognised as one of the most powerful and flexible statistical software packages, and it enables the user to apply many statistical techniques that would be impossible without such software to help implement such large data sets.

    It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.

    R is packed with powerful programming capabilities, but learning to use R in the real world can be overwhelming for even the most seasoned statisticians. This easy-to-follow guide explains how to use R for data processing and statistical analysis, and then, shows you how to present your data using compelling and informative graphics. You'll gain practical experience using R in a variety of settings and delve deeper into R's feature-rich toolset.Includes tips for the initial installation of RDemonstrates how to easily perform calculations on vectors, arrays, and lists of dataShows how to effectively visualize data using R's powerful graphics packagesGives pointers on how to find, install, and use add-on packages created by the R communityProvides tips on getting additional help from R mailing lists and websitesWhether you're just starting out with statistical analysis or are a procedural programming pro, "R For Dummies" is the book you need to get the most out of R.

    Discover the “hidden gold” in Python’s standard library and start writing clean and Pythonic code today. Who Should Read This Book: If you’re wondering which lesser known parts in Python you should know about, you’ll get a roadmap with this book. Discover cool (yet practical!) Python tricks and blow your coworkers’ minds in your next code review. If you’ve got experience with legacy versions of Python, the book will get you up to speed with modern patterns and features introduced in Python 3 and backported to Python 2. If you’ve worked with other programming languages and you want to get up to speed with Python, you’ll pick up the idioms and practical tips you need to become a confident and effective Pythonista. If you want to make Python your own and learn how to write clean and Pythonic code, you’ll discover best practices and little-known tricks to round out your knowledge. What Python Developers Say About The Book: "I kept thinking that I wished I had access to a book like this when I started learning Python many years ago." — Mariatta Wijaya, Python Core Developer"This book makes you write better Python code!" — Bob Belderbos, Software Developer at Oracle"Far from being just a shallow collection of snippets, this book will leave the attentive reader with a deeper understanding of the inner workings of Python as well as an appreciation for its beauty." — Ben Felder, Pythonista"It's like having a seasoned tutor explaining, well, tricks!" — Daniel Meyer, Sr. Desktop Administrator at Tesla Inc.

    But, as Mandi Walls explains in this Velocity report, DevOps is really about changing company culture—replacing traditional development and operations silos with collaborative teams of people from both camps. The DevOps movement has produced some efficient teams turning out better products faster. The tough part is initiating the change. This report outlines strategies for managers looking to go beyond tools to build a DevOps culture among their technical staff. Topics include: Documenting reasons for changing to DevOps before you commit Defining meaningful and achievable goals Finding a technical leader to be an evangelist, tools and process expert, and shepherd Starting with a non-critical but substantial pilot project Facilitating open communication among developers, QA engineers, marketers, and other professionals Realigning your team’s responsibilities and incentives Learning when to mediate disagreements and conflicts Download this free report and learn how to the DevOps approach can help you create a supportive team environment built on communication, respect, and trust. Mandi Walls is a Senior Consultant with Opscode.

    Backed by investors such as Larry Ellison and Tim Draper, Theranos sold shares in a fundraising round that valued the company at $9 billion, putting Holmes's worth at an estimated $4.7 billion. There was just one problem: The technology didn't work.For years, Holmes had been misleading investors, FDA officials, and her own employees. When Carreyrou, working at The Wall Street Journal, got a tip from a former Theranos employee and started asking questions, both Carreyrou and the Journal were threatened with lawsuits. Undaunted, the newspaper ran the first of dozens of Theranos articles in late 2015. By early 2017, the company's value was zero and Holmes faced potential legal action from the government and her investors. Here is the riveting story of the biggest corporate fraud since Enron, a disturbing cautionary tale set amid the bold promises and gold-rush frenzy of Silicon Valley.

    Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of their organization’s massive data sets and applying their findings to real-world business scenarios. From uncovering rich data sources to managing large amounts of data within hardware and software limitations, ensuring consistency in reporting, merging various data sources, and beyond, you’ll develop the know-how you need to effectively interpret data and tell a story that can be understood by anyone in your organization. Provides a background in data science fundamentals before moving on to working with relational databases and unstructured data and preparing your data for analysis Details different data visualization techniques that can be used to showcase and summarize your data Explains both supervised and unsupervised machine learning, including regression, model validation, and clustering techniques Includes coverage of big data processing tools like MapReduce, Hadoop, Dremel, Storm, and Spark It’s a big, big data world out there – let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.

    The algorithms you'll use most often as a programmer have already been discovered, tested, and proven. If you want to take a hard pass on Knuth's brilliant but impenetrable theories and the dense multi-page proofs you'll find in most textbooks, this is the book for you. This fully-illustrated and engaging guide makes it easy for you to learn how to use algorithms effectively in your own programs.Grokking Algorithms is a disarming take on a core computer science topic. In it, you'll learn how to apply common algorithms to the practical problems you face in day-to-day life as a programmer. You'll start with problems like sorting and searching. As you build up your skills in thinking algorithmically, you'll tackle more complex concerns such as data compression or artificial intelligence. Whether you're writing business software, video games, mobile apps, or system utilities, you'll learn algorithmic techniques for solving problems that you thought were out of your grasp. For example, you'll be able to:Write a spell checker using graph algorithmsUnderstand how data compression works using Huffman codingIdentify problems that take too long to solve with naive algorithms, and attack them with algorithms that give you an approximate answer insteadEach carefully-presented example includes helpful diagrams and fully-annotated code samples in Python. By the end of this book, you will know some of the most widely applicable algorithms as well as how and when to use them.

    In Turing’s Cathedral, George Dyson focuses on a small group of men and women, led by John von Neumann at the Institute for Advanced Study in Princeton, New Jersey, who built one of the first computers to realize Alan Turing’s vision of a Universal Machine. Their work would break the distinction between numbers that mean things and numbers that do things—and our universe would never be the same. Using five kilobytes of memory (the amount allocated to displaying the cursor on a computer desktop of today), they achieved unprecedented success in both weather prediction and nuclear weapons design, while tackling, in their spare time, problems ranging from the evolution of viruses to the evolution of stars. Dyson’s account, both historic and prophetic, sheds important new light on how the digital universe exploded in the aftermath of World War II. The proliferation of both codes and machines was paralleled by two historic developments: the decoding of self-replicating sequences in biology and the invention of the hydrogen bomb. It’s no coincidence that the most destructive and the most constructive of human inventions appeared at exactly the same time.  How did code take over the world? In retracing how Alan Turing’s one-dimensional model became John von Neumann’s two-dimensional implementation, Turing’s Cathedral offers a series of provocative suggestions as to where the digital universe, now fully three-dimensional, may be heading next.

    Now with twice the number of stories of stupidity coming to the hospital, emergency room and operating room. DR Payne has been taking care of patients for over 18 years in places from Alaska to the American South and she has seen her share of Stupid. Feel a little less stupid today after you read this book. Sections include: Stupid in Action Stupid in Word Stupid in Thought Stupid Baby Names Notable Tattoos A Final Thought

    Now that machine learning is thriving, 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 how to use a range of techniques, starting with simple Linear Regression and progressing to Deep Neural Networks. If you have some programming experience and you’re ready to code a machine learning project, this guide is for you.This hands-on book shows you how to use:Scikit-Learn, an accessible framework that implements many algorithms efficiently and serves as a great machine learning entry pointTensorFlow, a more complex library for distributed numerical computation, ideal for training and running very large neural networksPractical code examples that you can apply without learning excessive machine learning theory or algorithm details

    Digital technologies—with hardware, software, and networks at their core—will in the near future diagnose diseases more accurately than doctors can, apply enormous data sets to transform retailing, and accomplish many tasks once considered uniquely human.In The Second Machine Age MIT’s Erik Brynjolfsson and Andrew McAfee—two thinkers at the forefront of their field—reveal the forces driving the reinvention of our lives and our economy. As the full impact of digital technologies is felt, we will realize immense bounty in the form of dazzling personal technology, advanced infrastructure, and near-boundless access to the cultural items that enrich our lives.Amid this bounty will also be wrenching change. Professions of all kinds—from lawyers to truck drivers—will be forever upended. Companies will be forced to transform or die. Recent economic indicators reflect this shift: fewer people are working, and wages are falling even as productivity and profits soar.Drawing on years of research and up-to-the-minute trends, Brynjolfsson and McAfee identify the best strategies for survival and offer a new path to prosperity. These include revamping education so that it prepares people for the next economy instead of the last one, designing new collaborations that pair brute processing power with human ingenuity, and embracing policies that make sense in a radically transformed landscape.A fundamentally optimistic book, The Second Machine Age alters how we think about issues of technological, societal, and economic progress.