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

    With the 97 short and extremely useful tips for programmers in this book, you'll expand your skills by adopting new approaches to old problems, learning appropriate best practices, and honing your craft through sound advice.With contributions from some of the most experienced and respected practitioners in the industry--including Michael Feathers, Pete Goodliffe, Diomidis Spinellis, Cay Horstmann, Verity Stob, and many more--this book contains practical knowledge and principles that you can apply to all kinds of projects.A few of the 97 things you should know:"Code in the Language of the Domain" by Dan North"Write Tests for People" by Gerard Meszaros"Convenience Is Not an -ility" by Gregor Hohpe"Know Your IDE" by Heinz Kabutz"A Message to the Future" by Linda Rising"The Boy Scout Rule" by Robert C. Martin (Uncle Bob)"Beware the Share" by Udi Dahan

    Previously undocumented, these 23 patterns allow designers to create more flexible, elegant, and ultimately reusable designs without having to rediscover the design solutions themselves.The authors begin by describing what patterns are and how they can help you design object-oriented software. They then go on to systematically name, explain, evaluate, and catalog recurring designs in object-oriented systems. With Design Patterns as your guide, you will learn how these important patterns fit into the software development process, and how you can leverage them to solve your own design problems most efficiently. Each pattern describes the circumstances in which it is applicable, when it can be applied in view of other design constraints, and the consequences and trade-offs of using the pattern within a larger design. All patterns are compiled from real systems and are based on real-world examples. Each pattern also includes code that demonstrates how it may be implemented in object-oriented programming languages like C++ or Smalltalk.

    because the language seems like a good way to accomplish programming tasks that don't require the low-level bit handling power of C.-- Richard Bejtlich, TaoSecurityPython is a new and innovative scripting language. It is set to replace Perl as the programming language of choice for shell scripters, and for serious application developers who want a feature-rich, yet simple language to deploy their products.Dive Into Python is a hands-on guide to the Python language. Each chapter starts with a real, complete code sample, proceeds to pick it apart and explain the pieces, and then puts it all back together in a summary at the end.This is the perfect resource for you if you like to jump into languages fast and get going right away. If you're just starting to learn Python, first pick up a copy of Magnus Lie Hetland's Practical Python.

    This long-awaited revision contains changes throughout the text. There are new implementations of most of the major programming systems in the book, including the interpreters and compilers, and the authors have incorporated many small changes that reflect their experience teaching the course at MIT since the first edition was published. A new theme has been introduced that emphasizes the central role played by different approaches to dealing with time in computational models: objects with state, concurrent programming, functional programming and lazy evaluation, and nondeterministic programming. There are new example sections on higher-order procedures in graphics and on applications of stream processing in numerical programming, and many new exercises. In addition, all the programs have been reworked to run in any Scheme implementation that adheres to the IEEE standard.

    Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords?In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures

    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.

    As the words "at work" suggest, Peter Seibel focuses on how his interviewees tackle the day–to–day work of programming, while revealing much more, like how they became great programmers, how they recognize programming talent in others, and what kinds of problems they find most interesting. Hundreds of people have suggested names of programmers to interview on the Coders at Work web site: http://www.codersatwork.com. The complete list was 284 names. Having digested everyone’s feedback, we selected 16 folks who’ve been kind enough to agree to be interviewed:- Frances Allen: Pioneer in optimizing compilers, first woman to win the Turing Award (2006) and first female IBM fellow- Joe Armstrong: Inventor of Erlang- Joshua Bloch: Author of the Java collections framework, now at Google- Bernie Cosell: One of the main software guys behind the original ARPANET IMPs and a master debugger- Douglas Crockford: JSON founder, JavaScript architect at Yahoo!- L. Peter Deutsch: Author of Ghostscript, implementer of Smalltalk-80 at Xerox PARC and Lisp 1.5 on PDP-1- Brendan Eich: Inventor of JavaScript, CTO of the Mozilla Corporation - Brad Fitzpatrick: Writer of LiveJournal, OpenID, memcached, and Perlbal - Dan Ingalls: Smalltalk implementor and designer- Simon Peyton Jones: Coinventor of Haskell and lead designer of Glasgow Haskell Compiler- Donald Knuth: Author of The Art of Computer Programming and creator of TeX- Peter Norvig: Director of Research at Google and author of the standard text on AI- Guy Steele: Coinventor of Scheme and part of the Common Lisp Gang of Five, currently working on Fortress- Ken Thompson: Inventor of UNIX- Jamie Zawinski: Author of XEmacs and early Netscape/Mozilla hackerWhat you’ll learn:How the best programmers in the world do their jobWho is this book for?Programmers interested in the point of view of leaders in the field. Programmers looking for approaches that work for some of these outstanding programmers.

    Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.Each chapter will cover a different technique in a spreadsheet so you can follow along: - Mathematical optimization, including non-linear programming and genetic algorithms- Clustering via k-means, spherical k-means, and graph modularity- Data mining in graphs, such as outlier detection- Supervised AI through logistic regression, ensemble models, and bag-of-words models- Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation- Moving from spreadsheets into the R programming languageYou get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.

    With this hands-on book, you’ll learn Flask from the ground up by developing a complete social blogging application step-by-step. Author Miguel Grinberg walks you through the framework’s core functionality, and shows you how to extend applications with advanced web techniques such as database migration and web service communication.Rather than impose development guidelines as other frameworks do, Flask leaves the business of extensions up to you. If you have Python experience, this book shows you how to take advantage of that creative freedom.- Learn Flask’s basic application structure and write an example app- Work with must-have components—templates, databases, web forms, and email support- Use packages and modules to structure a large application that scales- Implement user authentication, roles, and profiles- Build a blogging feature by reusing templates, paginating item lists, and working with rich text- Use a Flask-based RESTful API to expose app functionality to smartphones, tablets, and other third-party clients- Learn how to run unit tests and enhance application performance- Explore options for deploying your web app to a production server

    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.

    Many examples are taken from real-world projects. The book focuses on high-level design as well as the gritty details of the Python syntax. The provided exercises inspire the reader to think about his or her own code, rather than providing solved problems. If you're new to Object Oriented Programming techniques, or if you have basic Python skills and wish to learn in depth how and when to correctly apply Object Oriented Programming in Python, this is the book for you. If you are an object-oriented programmer for other languages, you too will find this book a useful introduction to Python, as it uses terminology you are already familiar with. Python 2 programmers seeking a leg up in the new world of Python 3 will also find the book beneficial, and you need not necessarily know Python 2.

    Whether you're building the next killer app, investing in a startup, or simply curious about the technology, this practical book is essential reading.Bitcoin, the first successful decentralized digital currency, is still in its infancy and it's already spawned a multi-billion dollar global economy. This economy is open to anyone with the knowledge and passion to participate. Mastering Bitcoin provides you with the knowledge you need (passion not included).This book includes:A broad introduction to bitcoin--ideal for non-technical users, investors, and business executivesAn explanation of the technical foundations of bitcoin and cryptographic currencies for developers, engineers, and software and systems architectsDetails of the bitcoin decentralized network, peer-to-peer architecture, transaction lifecycle, and security principlesOffshoots of the bitcoin and blockchain inventions, including alternative chains, currencies, and applicationsUser stories, analogies, examples, and code snippets illustrating key technical concepts

    The principal enhancement in Java 8 was the addition of functional programming constructs to Java's object-oriented roots. Java 7, 8, and 9 also introduced language features, such as the try-with-resources statement, the diamond operator for generic types, default and static methods in interfaces, the @SafeVarargs annotation, and modules. New library features include pervasive use of functional interfaces and streams, the java.time package for manipulating dates and times, and numerous minor enhancements such as convenience factory methods for collections. In this new edition of Effective Java, Bloch updates the work to take advantage of these new language and library features, and provides specific best practices for their use. Java's increased support for multiple paradigms increases the need for best-practices advice, and this book delivers. As in previous editions, each chapter consists of several "items," each presented in the form of a short, standalone 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. Coverage includes:Updated techniques and best practices on classic topics, including objects, classes, methods, libraries, and generics How to avoid the traps and pitfalls of commonly misunderstood subtleties of the platform Focus on the language and its most fundamental libraries, such as java.lang and java.util

    This book surveys the most important computer algorithms currently in use and provides a full treatment of data structures and algorithms for sorting, searching, graph processing, and string processing -- including fifty algorithms every programmer should know. In this edition, new Java implementations are written in an accessible modular programming style, where all of the code is exposed to the reader and ready to use.The algorithms in this book represent a body of knowledge developed over the last 50 years that has become indispensable, not just for professional programmers and computer science students but for any student with interests in science, mathematics, and engineering, not to mention students who use computation in the liberal arts.The companion web site, algs4.cs.princeton.edu contains An online synopsis Full Java implementations Test data Exercises and answers Dynamic visualizations Lecture slides Programming assignments with checklists Links to related material The MOOC related to this book is accessible via the "Online Course" link at algs4.cs.princeton.edu. The course offers more than 100 video lecture segments that are integrated with the text, extensive online assessments, and the large-scale discussion forums that have proven so valuable. Offered each fall and spring, this course regularly attracts tens of thousands of registrants.Robert Sedgewick and Kevin Wayne are developing a modern approach to disseminating knowledge that fully embraces technology, enabling people all around the world to discover new ways of learning and teaching. By integrating their textbook, online content, and MOOC, all at the state of the art, they have built a unique resource that greatly expands the breadth and depth of the educational experience.