But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications.Authors Jeremy Howard and Sylvain Gugger show you how to train a model on a wide range of tasks using fastai and PyTorch. You'll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes.Train models in computer vision, natural language processing, tabular data, and collaborative filteringLearn the latest deep learning techniques that matter most in practiceImprove accuracy, speed, and reliability by understanding how deep learning models workDiscover how to turn your models into web applicationsImplement deep learning algorithms from scratchConsider the ethical implications of your work

    

    

    Without machine learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation, and email spam filtering wouldn't be possible, and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks.Covering the entry-level topics needed to get you familiar with the basic concepts of machine learning, this guide quickly helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality. Whether you're maddened by the math behind machine learning, apprehensive about AI, perplexed by preprocessing data--or anything in between--this guide makes it easier to understand and implement machine learning seamlessly.Grasp how day-to-day activities are powered by machine learning Learn to 'speak' certain languages, such as Python and R, to teach machines to perform pattern-oriented tasks and data analysis Learn to code in R using R Studio Find out how to code in Python using Anaconda Dive into this complete beginner's guide so you are armed with all you need to know about machine learning!

    Siracusa's overview, wrap-up, and critique of everything new in OS X 10.10 Yosemite.

    Proceed step-by-step through five progressively more complex web applications: from a "Hello World" app all the way to a robust Newspaper app with a custom user model, complete user authentication flow, foreign key relationships, and more. Learn current best practices around class-based views, templates, urls, user authentication, testing, and deployment. The material is up-to-date with the latest versions of both Django (2.0) and Python (3.6). TABLE OF CONTENTS: * Introduction * Chapter 1: Initial Setup * Chapter 2: Hello World app * Chapter 3: Pages app * Chapter 4: Message Board app * Chapter 5: Blog app * Chapter 6: Forms * Chapter 7: User Accounts * Chapter 8: Custom User Model * Chapter 9: User Authentication * Chapter 10: Bootstrap * Chapter 11: Password Change and Reset * Chapter 12: Email * Chapter 13: Newspaper app * Chapter 14: Permissions and Authorizations * Chapter 15: Comments * Conclusion

    You'll also receive updates when significant changes are made, as well as the final ebook version. Embedded Android is for Developers wanting to create embedded systems based on Android and for those wanting to port Android to new hardware, or creating a custom development environment. Hackers and moders will also find this an indispensible guide to how Android works.

    Essentially, you are estimating a probability, but then updating that estimate based on other things that you know. This book is designed to give you an intuitive understanding of how to use Bayes Theorem. It starts with the definition of what Bayes Theorem is, but the focus of the book is on providing examples that you can follow and duplicate. Most of the examples are calculated in Excel, which is useful for updating probability if you have dozens or hundreds of data points to roll in.

    It is very readable and the stress many beginners face about memorizing arcane syntax typically presented by other programming languages will not affect you at all. Conversely, you will be able to concentrate on learning concepts and paradigms of programming. This book shall introduce you to an easy way to learn Python in just 7 days and in this time, be able to complete your own projects! By reading the book and implementing what you learn herein, you will realize just why major institutions like NASA, Google, Mozilla, Yahoo, Dropbox, IBM, Facebook and many others prefer to use python in their core products, services and business processes. Let

    It's not intended to replace manufacturer's or user's manuals, but rather complements and expands on the information contained in these materials. All topics are covered in small segments. Students systematically carry out a wide range of generic programming exercises and assignments. All of the information about PLCs has been updated.

    You can think of Python as your tool to solve problems that are far beyond the capability of a spreadsheet. It is an easy-to-use and easy-to learn programming language that is freely available on Windows, Macintosh, and Linux computers. There are free downloadable copies of this book in various electronic formats and a self-paced free online course where you can explore the course materials. All the supporting materials for the book are available under open and remixable licenses. This book is designed to teach people to program even if they have no prior experience.

    This book covers it all! Encrypting your private files, securing your PC, masking your online footsteps, and all while giving you peace of mind with TOTAL 100% ANONYMITY. Don't waste months scouring the internet for info. Just read this! You'll be hooked in five minutes. It's all here: CIA techniques, how the NSA catches Tor users, Truecrypt and the FBI, nuking tracking cookies, private browsing, preventing identity theft. I will show you: -How to Be Anonymous Online -Step by Step Guides for Tor, Freenet, I2P, VPNs, Usenet and more -Browser Fingerprinting -Anti-Hacking and Counter-Forensic Techniques -Photo & Video Metadata -How to Encrypt Files (I make this super simple) -How to Defeat NSA Spying -How to Browse the Deep Web -How to Protect Your Identity -How to Hide Anything! You've probably read How to Be Invisible by J. J. Luna and Incognito Toolkit by Rob Robideau, and while they are fine books, you need this companion piece to take it to the next level!

    We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services – machine learning makes it all possible.Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively.This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world

    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.

    Using sample charts, graphics, case studies, and war stories, Yankee Group Security Expert Andrew Jaquith demonstrates exactly how to establish effective metrics based on your organization's unique requirements. You'll discover how to quantify hard-to-measure security activities, compile and analyze all relevant data, identify strengths and weaknesses, set cost-effective priorities for improvement, and craft compelling messages for senior management. Security Metrics successfully bridges management's quantitative viewpoint with the nuts-and-bolts approach typically taken by security professionals. It brings together expert solutions drawn from Jaquith's extensive consulting work in the software, aerospace, and financial services industries, including new metrics presented nowhere else. You'll learn how to: - Replace nonstop crisis response with a systematic approach to security improvement - Understand the differences between "good" and "bad" metrics - Measure coverage and control, vulnerability management, password quality, patch latency, benchmark scoring, and business-adjusted risk - Quantify the effectiveness of security acquisition, implementation, and other program activities - Organize, aggregate, and analyze your data to bring out key insights - Use visualization to understand and communicate security issues more clearly - Capture valuable data from firewalls and antivirus logs, third-party auditor reports, and other resources - Implement balanced scorecards that present compact, holistic views of organizational security effectiveness Whether you're an engineer or consultant responsible for security and reporting to management-or an executive who needs better information for decision-making-Security Metrics is the resource you have been searching for. Andrew Jaquith, program manager for Yankee Group's Security Solutions and Services Decision Service, advises enterprise clients on prioritizing and managing security resources. He also helps security vendors develop product, service, and go-to-market strategies for reaching enterprise customers. He co-founded @stake, Inc., a security consulting pioneer acquired by Symantec Corporation in 2004. His application security and metrics research has been featured in CIO, CSO, InformationWeek, IEEE Security and Privacy, and The Economist. Foreword Preface Acknowledgments About the Author Chapter 1 Introduction: Escaping the Hamster Wheel of Pain Chapter 2 Defining Security Metrics Chapter 3 Diagnosing Problems and Measuring Technical Security Chapter 4 Measuring Program Effectiveness Chapter 5 Analysis Techniques Chapter 6 Visualization Chapter 7 Automating Metrics Calculations Chapter 8 Designing Security Scorecards Index