More than 250 challenging practice questions have been completely revised to closely model the format, tone, topics, and difficulty of the real exam. An integrated study system based on proven pedagogy, exam coverage includes step-by-step exercises, special Exam Watch notes, On-the-Job elements, and Self Tests with in-depth answer explanations to help reinforce and teach practical skills.Praise for the author:"Finally A Java certification book that explains everything clearly. All you need to pass the exam is in this book."--Solveig Haugland, Technical Trainer and Former Sun Course Developer"Who better to write a Java study guide than Kathy Sierra, the reigning queen of Java instruction? Kathy Sierra has done it again--here is a study guide that almost guarantees you a certification "--James Cubeta, Systems Engineer, SGI"The thing I appreciate most about Kathy is her quest to make us all remember that we are teaching people and not just lecturing about Java. Her passion and desire for the highest quality education that meets the needs of the individual student is positively unparalleled at SunEd. Undoubtedly there are hundreds of students who have benefited from taking Kathy's classes."--Victor Peters, founder Next Step Education & Software Sun Certified Java Instructor"I want to thank Kathy for the EXCELLENT Study Guide. The book is well written, every concept is clearly explained using a real life example, and the book states what you specifically need to know for the exam. The way it's written, you feel that you're in a classroom and someone is actually teaching you the difficult concepts, but not in a dry, formal manner. The questions at the end of the chapters are also REALLY good, and I am sure they will help candidates pass the test. Watch out for this Wickedly Smart book."-Alfred Raouf, Web Solution Developer, Kemety.Net"The Sun Certification exam was certainly no walk in the park but Kathy's material allowed me to not only pass the exam, but Ace it "--Mary Whetsel, Sr. Technology Specialist, Application Strategy and Integration, The St. Paul Companies

    It provides guidance to help customers apply best practices in the design, delivery, and maintenance of secure AWS environments. This documentation is offered for free here as a Kindle book, or you can read it in PDF format at https://aws.amazon.com/whitepapers/.

    This book covers relevant geometric principles and how to represent objects algebraically so they can be computed and applied. Recent major developments in the theory and practice of scene reconstruction are described in detail in a unified framework. Richard Hartley and Andrew Zisserman provide comprehensive background material and explain how to apply the methods and implement the algorithms. First Edition HB (2000): 0-521-62304-9

    This book teaches basic Artificial Intelligence algorithms such as dimensionality, distance metrics, clustering, error calculation, hill climbing, Nelder Mead, and linear regression. These are not just foundational algorithms for the rest of the series, but are very useful in their own right. The book explains all algorithms using actual numeric calculations that you can perform yourself. Artificial Intelligence for Humans is a book series meant to teach AI to those without an extensive mathematical background. The reader needs only a knowledge of basic college algebra or computer programming—anything more complicated than that is thoroughly explained. Every chapter also includes a programming example. Examples are currently provided in Java, C#, R, Python and C. Other languages planned.

    

    At $29 and cheaper than a good pizza, you will get the book in its current partial form, along with all future chapters, updates, and fixes for free. As of the day I wrote this description, Code Bright had 130 pages and was just getting started. To give you some perspective on how detailed it is, Code Happy was 127 pages in its complete state. Want to know more? Carry on reading.Welcome back to Laravel. Last year I wrote a book about the Laravel PHP framework. It started as a collection of tutorials on my blog, and eventually became a full book. I definitely didn’t expect it to be as popular as it was. Code Happy has sold almost 3000 copies, and is considered to be one of the most valuable resourcesfor learning the Laravel framework.Code Bright is the spiritual successor to Code Happy. The framework has grown a lot in the past year, and has changed enough to merit a new title. With Code Bright I hope to improve on Code Happy with every way, my goal is, to once again, build the most comprehensive learning experience for the framework. Oh, and to still be funny. That’s very important to me.Laravel Code Bright will contain a complete learning experience for all of the framework’s features. The style of writing will make it approachable for beginners, and a wonderful reference resource for experienced developers alike.You see, people have told me that they enjoyed reading Code Happy, not only for its educational content, but for its humour, and for my down to earth writing style. This is very important to me. I like to write my books as if we were having a conversation in a bar.When I wrote Code Happy last year, I was simply a framework enthusiast. One of the first to share information about the framework. However, since then I have become a committed member of the core development team. Working directly with the framework author to make Laravel a wonderful experience for the developers of the world.One other important feature of both books, is that they are published while in progress. This means that the book is available in an incomplete state, but will grow over time into a complete title. All future updates will be provided for free.What this means is that I don’t have to worry about deadlines, or a fixed point of completion. It leads to less stress and better writing. If I think of a better way to explain something, I can go back and change it. In a sense, the book will never be completed. I can constantly add more information to it, until it becomes the perfect resource.Given that this time I am using the majority of my spare time to write the title (yes, I have a full time job too!), I have raised the price a little to justify my invested time. I was told by many of my past readers that they found the previous title very cheap for the resource that it grew into, so if you are worried about the new price, then let me remind you what you will get for your 29 bucks.The successor to Code Happy, seen by many as the #1 learning resource for the Laravel PHP framework.An unending source of information, chapters will be constantly added as needed until the book becomes a giant vault of framework knowledge.Comedy, and a little cheesy, but very friendly writing.

    according to an artificial intelligence trained by scientist Janelle Shane, creator of the popular blog "AI Weirdness." She creates silly AIs that learn how to name paint colors, create the best recipes, and even flirt (badly) with humans--all to understand the technology that governs so much of our daily lives.We rely on AI every day for recommendations, for translations, and to put cat ears on our selfie videos. We also trust AI with matters of life and death, on the road and in our hospitals. But how smart is AI really, and how does it solve problems, understand humans, and even drive self-driving cars?Shane delivers the answers to every AI question you've ever asked, and some you definitely haven't--like, how can a computer design the perfect sandwich? What does robot-generated Harry Potter fan-fiction look like? And is the world's best Halloween costume really "Vampire Hog Bride"?In this smart, often hilarious introduction to the most interesting science of our time, Shane shows how these programs learn, fail, and adapt--and how they reflect the best and worst of humanity. You Look Like a Thing and I Love You is the perfect book for anyone curious about what the robots in our lives are thinking.

    Each idiom comes with a detailed description, example code showing the "wrong" way to do it, and code for the idiomatic, "Pythonic" alternative. *This version of the book is for Python 2.7.3+. There is also a Python 3.3+ version available.* "Writing Idiomatic Python" contains the most common and important Python idioms in a format that maximizes identification and understanding. Each idiom is presented as a recommendation to write some commonly used piece of code. It is followed by an explanation of why the idiom is important. It also contains two code samples: the "Harmful" way to write it and the "Idiomatic" way. * The "Harmful" way helps you identify the idiom in your own code. * The "Idiomatic" way shows you how to easily translate that code into idiomatic Python. This book is perfect for you: * If you're coming to Python from another programming language * If you're learning Python as a first programming language * If you're looking to increase the readability, maintainability, and correctness of your Python code What is "Idiomatic" Python? Every programming language has its own idioms. Programming language idioms are nothing more than the generally accepted way of writing a certain piece of code. Consistently writing idiomatic code has a number of important benefits: * Others can read and understand your code easily * Others can maintain and enhance your code with minimal effort * Your code will contain fewer bugs * Your code will teach others to write correct code without any effort on your part

    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

    Explore applying the Hexagonal Architecture within your application, whether within an open source framework or your own bespoke system. Finally, look into integrating Bounded Contexts, using REST and Messaging approaches.

    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

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

    In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

    Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.