His parents’ divorce set the stage for a downward spiral of self-destruction. The pressure he felt to keep his family together coupled with a deep desire to “fit in” fueled his experimentation with drugs and alcohol. His descent from upper-middle class teen with a promising future to the depths of heroin addiction left him bankrupt in every imaginable sense of the word. Soon, he was fully immersed in the dark underbelly of society and on the brink of death. Finding his way out of the abyss after ten years was neither quick nor easy. A twelve-step program of recovery and the practice of yoga provided the guiding lights toward a new path. Taylor does much more than share his story in A Way from Darkness; he invites the reader to find healing through community, Ashtanga yoga, and ultimately, acceptance.

    It's for everyone with a stake in the outcome of a development project--and especially for those without formal software project management training. That includes top managers, executives, clients, investors, end-user representatives, project managers, and technical leads. Here you'll find guidance from the acclaimed author of the classics CODE COMPLETE and RAPID DEVELOPMENT. Steve McConnell draws on solid research and a career's worth of hard-won experience to map the surest path to your goal--what he calls "one specific approach to software development that works pretty well most of the time for most projects." Nineteen chapters in four sections cover the concepts and strategies you need for mastering the development process, including planning, design, management, quality assurance, testing, and archiving. For newcomers and seasoned project managers alike, SOFTWARE PROJECT SURVIVAL GUIDE draws on a vast store of techniques to create an elegantly simplified and reliable framework for project management success. So don't worry about wandering among complex sets of project management techniques that require years to sort out and master. SOFTWARE PROJECT SURVIVAL GUIDE goes straight to the heart of the matter to help your projects succeed. And that makes it a required addition to every professional's bookshelf.

    Instead of relying on another attacker's tools, this book will teach you to forge your own weapons using the Python programming language. This book demonstrates how to write Python scripts to automate large-scale network attacks, extract metadata, and investigate forensic artifacts. It also shows how to write code to intercept and analyze network traffic using Python, craft and spoof wireless frames to attack wireless and Bluetooth devices, and how to data-mine popular social media websites and evade modern anti-virus.

    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.

    This practical guide shows you how to be productive with this tool quickly, whether you're a developer deploying code to production or a system administrator looking for a better automation solution.Author Lorin Hochstein shows you how to write playbooks (Ansible's configuration management scripts), manage remote servers, and explore the tool's real power: built-in declarative modules. You'll discover that Ansible has the functionality you need and the simplicity you desire.Understand how Ansible differs from other configuration management systemsUse the YAML file format to write your own playbooksLearn Ansible's support for variables and factsWork with a complete example to deploy a non-trivial applicationUse roles to simplify and reuse playbooksMake playbooks run faster with ssh multiplexing, pipelining, and parallelismDeploy applications to Amazon EC2 and other cloud platformsUse Ansible to create Docker images and deploy Docker containers

    During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.Companion wiki — the book has a continuously updated wiki that extends some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources.Flexible price and formats — choose from a variety of formats and price options: Kindle, hardcover, paperback, EPUB, PDF. If you buy an EPUB or a PDF, you decide the price you pay!Read first, buy later — download book chapters for free, read them and share with your friends and colleagues. Only if you liked the book or found it useful in your work, study or business, then buy it.

    With a focus on core concepts and best practices rather than on individual languages, platforms, or technologies, Web Scalability for Startup Engineers describes how infrastructure and software architecture work together to support a scalable environment.You'll learn, step by step, how scalable systems work and how to solve common challenges. Helpful diagrams are included throughout, and real-world examples illustrate the concepts presented. Even if you have limited time and resources, you can successfully develop and deliver robust, scalable web applications with help from this practical guide.Learn the key principles of good software design required for scalable systemsBuild the front-end layer to sustain the highest levels of concurrency and request ratesDesign and develop web services, including REST-ful APIsEnable a horizontally scalable data layerImplement caching best practicesLeverage asynchronous processing, messaging, and event-driven architectureStructure, index, and store data for optimized searchExplore other aspects of scalability, such as automation, project management, and agile teams

    But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.Topics include:Statistical inference, exploratory data analysis, and the data science processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and HadoopDoing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

    Kerth guides readers through productive, empowering retrospectives of project performance.Whether your shop calls them postmortems or postpartums or something else, project retrospectives offer organizations a formal method for preserving the valuable lessons learned from the successes and failures of every project. These lessons and the changes identified by the community will foster stronger teams and savings on subsequent efforts.For a retrospective to be effective and successful, though, it needs to be safe. Kerth shows facilitators and participants how to defeat the fear of retribution and establish an air of mutual trust. One tool is Kerth's Prime Directive:Regardless of what we discover, we must understand and truly believe that everyone did the best job he or she could, given what was known at the time, his or her skills and abilities, the resources available, and the situation at hand.Applying years of experience as a project retrospective facilitator for software organizations, Kerth reveals his secrets for managing the sensitive, often emotionally charged issues that arise as teams relive and learn from each project.Don't move on to your next project without consulting and using this readable, practical handbook. Each member of your team will be better prepared for the next deadline.

    Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

    "Machine learning," the process of automating tasks once considered the domain of highly-trained analysts and mathematicians, is the key to efficiently extracting useful information from this sea of raw data. Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. In it, the author uses the flexible Python programming language to show how to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.

    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.

    The book will also be extremely useful for those preparing for other MBA entrance examinations like XAT, SNAP, CMAT, NMAT, etc. Quantitative Aptitude is quite challenging component of the CAT question paper and the other mentioned MBA entrance examinations. In his inimitable style, Arun Sharma, an acknowledged authority on the topic, provides a comprehensive package of theory and practice problems to enable aspirants to attempt questions with extra speed and confidence.

    

    Some stories will make you smile, somewill bring tears to your eyes, some may make your heart skip a beat—butall of them will renew your faith in the power of friendship.