List books in category Computers & Technology / Machine Theory

  • Numerical computing with IEEE floating point arithmetic

    Numerical computing with IEEE floating point arithmetic
    Michael L. Overton

    This title provides an easily accessible yet detailed discussion of IEEE Std 754-1985, arguably the most important standard in the computer industry. The result of an unprecedented cooperation between academic computer scientists and the cutting edge of industry, it is supported by virtually every modern computer. Other topics include the floating point architecture of the Intel microprocessors and a discussion of programming language support for the standard.

  • Statistical Relational Artificial Intelligence: Logic, Probability, and Computation

    Statistical Relational Artificial Intelligence: Logic, Probability, and Computation
    Luc De Raedt

    An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks.

  • Yii Rapid Application Development Hotshot

    Yii Rapid Application Development Hotshot
    Lauren J. O’Meara

    Practical, real world example projects. Start with the topics that grab your attention or work through each project in sequence. This book is for intermediate to advanced level PHP programmers who want to master Yii.

  • Machine Learning for Hackers: Case Studies and Algorithms to Get You Started

    Machine Learning for Hackers: Case Studies and Algorithms to Get You Started
    Drew Conway

    If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.Develop a naïve Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a “whom to follow” recommendation system from Twitter data

  • Active Learning

    Active Learning
    Burr Settles

    The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose "queries," usually in the form of unlabeled data instances to be labeled by an "oracle" (e.g., a human annotator) that already understands the nature of the problem. This sort of approach is well-motivated in many modern machine learning and data mining applications, where unlabeled data may be abundant or easy to come by, but training labels are difficult, time-consuming, or expensive to obtain. This book is a general introduction to active learning. It outlines several scenarios in which queries might be formulated, and details many query selection algorithms which have been organized into four broad categories, or "query selection frameworks." We also touch on some of the theoretical foundations of active learning, and conclude with an overview of the strengths and weaknesses of these approaches in practice, including a summary of ongoing work to address these open challenges and opportunities. Table of Contents: Automating Inquiry / Uncertainty Sampling / Searching Through the Hypothesis Space / Minimizing Expected Error and Variance / Exploiting Structure in Data / Theory / Practical Considerations

  • Learning scikit-learn: Machine Learning in Python

    Learning scikit-learn: Machine Learning in Python
    Raul Garreta

    The book adopts a tutorial-based approach to introduce the user to Scikit-learn.If you are a programmer who wants to explore machine learning and data-based methods to build intelligent applications and enhance your programming skills, this the book for you. No previous experience with machine-learning algorithms is required.

  • Thoughtful Machine Learning: A Test-Driven Approach

    Thoughtful Machine Learning: A Test-Driven Approach
    Matthew Kirk

    Learn how to apply test-driven development (TDD) to machine-learning algorithms—and catch mistakes that could sink your analysis. In this practical guide, author Matthew Kirk takes you through the principles of TDD and machine learning, and shows you how to apply TDD to several machine-learning algorithms, including Naive Bayesian classifiers and Neural Networks.Machine-learning algorithms often have tests baked in, but they can’t account for human errors in coding. Rather than blindly rely on machine-learning results as many researchers have, you can mitigate the risk of errors with TDD and write clean, stable machine-learning code. If you’re familiar with Ruby 2.1, you’re ready to start.Apply TDD to write and run tests before you start codingLearn the best uses and tradeoffs of eight machine learning algorithmsUse real-world examples to test each algorithm through engaging, hands-on exercisesUnderstand the similarities between TDD and the scientific method for validating solutionsBe aware of the risks of machine learning, such as underfitting and overfitting dataExplore techniques for improving your machine-learning models or data extraction

  • Instant Android Systems Development How-To

    Instant Android Systems Development How-To
    Earlence Fernandes

    Filled with practical, step-by-step instructions and clear explanations for the most important and useful tasks.This is a how-to book with practical, coded examples which are well explained.This book is for seasoned Android SDK programmers. Knowledge of Java, Linux, and C is assumed. Certain Operating System concepts like processes, threads, shared memory, and inter process communication is also assumed, but the book provides necessary background before any obscure topics are introduced.

  • Instant Oracle GoldenGate

    Instant Oracle GoldenGate
    Tony Bruzzese

    Filled with practical, step-by-step instructions and clear explanations for the most important and useful tasks. Get the job done and learn as you go. A how-To book with practical recipes accompanied with rich screenshots for easy comprehension.This is a Packt Instant How-to guide, which provides concise and clear recipes for performing the core task of replication using Oracle GoldenGate.The book is aimed at DBAs from any of popular RDBMS systems such as Oracle, SQL Server, Teradata, Sybase, and so on. The level of detail provides quick applicability to beginners and a handy review for more advanced administrators.

  • Continuous Delivery and DevOps: a QuickStart Guide

    Continuous Delivery and DevOps: a QuickStart Guide
    Paul Swartout

    This book is both a practical and theoretical guide detailing how to implement continuous delivery and Devops to consistently ship quality software quickly. Whether you are a freelance software developer, a system administrator working within a corporate business, an IT project manager or a CTO in a startup you will have a common problem; regularly shipping quality software is painful. It needn't be. This book is for anyone who wants to understand how to ship quality software regularly without the pain.

  • Building a Recommendation System with R

    Building a Recommendation System with R
    Suresh K. Gorakala

    Learn the art of building robust and powerful recommendation engines using RAbout This BookLearn to exploit various data mining techniquesUnderstand some of the most popular recommendation techniquesThis is a step-by-step guide full of real-world examples to help you build and optimize recommendation enginesWho This Book Is ForIf you are a competent developer with some knowledge of machine learning and R, and want to further enhance your skills to build recommendation systems, then this book is for you.What You Will LearnGet to grips with the most important branches of recommendationUnderstand various data processing and data mining techniquesEvaluate and optimize the recommendation algorithmsPrepare and structure the data before building modelsDiscover different recommender systems along with their implementation in RExplore various evaluation techniques used in recommender systemsGet to know about recommenderlab, an R package, and understand how to optimize it to build efficient recommendation systemsIn DetailA recommendation system performs extensive data analysis in order to generate suggestions to its users about what might interest them. R has recently become one of the most popular programming languages for the data analysis. Its structure allows you to interactively explore the data and its modules contain the most cutting-edge techniques thanks to its wide international community. This distinctive feature of the R language makes it a preferred choice for developers who are looking to build recommendation systems.The book will help you understand how to build recommender systems using R. It starts off by explaining the basics of data mining and machine learning. Next, you will be familiarized with how to build and optimize recommender models using R. Following that, you will be given an overview of the most popular recommendation techniques. Finally, you will learn to implement all the concepts you have learned throughout the book to build a recommender system.Style and approachThis is a step-by-step guide that will take you through a series of core tasks. Every task is explained in detail with the help of practical examples.

  • Python Machine Learning: A Practical Beginner s Guide to Understanding Machine Learning, Deep Learning and Neural Networks with Python, Scikit-Learn, Tensorflow and Keras

    Python Machine Learning: A Practical Beginner’s Guide to Understanding Machine Learning, Deep Learning and Neural Networks with Python, Scikit-Learn, Tensorflow and Keras
    Brandon Railey

    ★☆Have you come across the terms machine learning and neural networks in most articles you have recently read? Do you also want to learn how to build a machine learning model that will answer your questions within a blink of your eyes?☆★ If you responded yes to any of the above questions, you have come to the right place.Machine learning is an incredibly dense topic. It's hard to imagine condensing it into an easily readable and digestible format. However, this book aims to do exactly that.Machine learning and artificial intelligence have been used in different machines and applications to improve the user's experience. One can also use machine learning to make data analysis and predicting the output for some data sets easy. All you need to do is choose the right algorithm, train the model and test the model before you apply it on any real-world tool. It is that simple isn't it?★★Apart from this, you will also learn more about★★ ♦ The Different Types Of Learning Algorithm That You Can Expect To Encounter♦ The Numerous Applications Of Machine Learning And Deep Learning♦ The Best Practices For Picking Up Neural Networks♦ What Are The Best Languages And Libraries To Work With♦ The Various Problems That You Can Solve With Machine Learning Algorithms♦ And much more…Well, you can do it faster if you use Python. This language has made it easy for any user, even an amateur, to build a strong machine learning model since it has numerous directories and libraries that make it easy for one to build a model. Do you want to know how to build a machine learning model and a neural network?So, what are you waiting for? Grab a copy of this book now!

  • 골빈해커의 3분 딥러닝: 텐서플로 코드로 맛보는 CNN, AE, GAN, RNN, DQN (+ Inception)

    골빈해커의 3분 딥러닝: 텐서플로 코드로 맛보는 CNN, AE, GAN, RNN, DQN (+ Inception)
    김진중(골빈해커)

    텐서플로 코드로 맛보는 딥러닝 핵심 개념! 이 책은 신경망 기초부터 CNN, Autoencoder, GAN, RNN, DQN까지 딥러닝의 가장 기본이 되는 모델들을 직접 구현하며 몸으로 익히도록 구성했습니다. 이론을 깊이 파헤치기보다는 다양한 딥러닝 모델의 기초 개념과 기본적인 텐서플로 사용법을 학습하는 데 초점을 두고, 각 모델의 논문에 수록된 복잡한 코드들을 그 핵심이 잘 드러나도록 재구현했습니다. 간결해진 예제들이 여러분을 딥러닝과 텐서플로의 세계로 즐겁고 편안히 모실 것입니다.이론보다는 실전! 몸으로 먼저 익히는 딥러닝! “한동안 좌절하던 중, 텐서플로 예제나 한번 돌려보자 싶더군요. 그런데 예제들을 돌려보고 나니 어렵게만 느껴지던 강좌들이 어느 정도 이해되는 것이었습니다! 그래서 깨달았죠. “아, 나 같은 사람은 코드로 먼저 공부하는 게 좋겠다!” … 이 책은 딥러닝/머신러닝을 배우고 싶지만, 수식만 나오면 울렁거려서 책을 덮는 저 같은 프로그래머에게 가장 적합합니다. 더불어 딥러닝/머신러닝을 공부하는 학생이나 연구자, 혹은 이론을 먼저 공부한 개발자 중 텐서플로를 써보고 싶은 분께도 좋은 가이드가 될 것입니다.”_ ‘서문’ 중에서 ★ 주요 내용텐서플로 프로그래밍 101기본 신경망 구현텐서보드와 모델 재사용헬로 딥러닝, MNIST이미지 인식의 은총알, CNN대표적 비지도 학습법, Autoencoder딥러닝의 미래, GAN번역과 챗봇 모델의 기본, RNN구글의 핵심 이미지 인식 모델, Inception딥마인드가 개발한 강화학습, DQN

  • Planning with Markov Decision Processes: An AI Perspective

    Planning with Markov Decision Processes: An AI Perspective
    Mausam

    Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics. They are the framework of choice when designing an intelligent agent that needs to act for long periods of time in an environment where its actions could have uncertain outcomes. MDPs are actively researched in two related subareas of AI, probabilistic planning and reinforcement learning. Probabilistic planning assumes known models for the agent's goals and domain dynamics, and focuses on determining how the agent should behave to achieve its objectives. On the other hand, reinforcement learning additionally learns these models based on the feedback the agent gets from the environment. This book provides a concise introduction to the use of MDPs for solving probabilistic planning problems, with an emphasis on the algorithmic perspective. It covers the whole spectrum of the field, from the basics to state-of-the-art optimal and approximation algorithms. We first describe the theoretical foundations of MDPs and the fundamental solution techniques for them. We then discuss modern optimal algorithms based on heuristic search and the use of structured representations. A major focus of the book is on the numerous approximation schemes for MDPs that have been developed in the AI literature. These include determinization-based approaches, sampling techniques, heuristic functions, dimensionality reduction, and hierarchical representations. Finally, we briefly introduce several extensions of the standard MDP classes that model and solve even more complex planning problems. Table of Contents: Introduction / MDPs / Fundamental Algorithms / Heuristic Search Algorithms / Symbolic Algorithms / Approximation Algorithms / Advanced Notes

  • Data Mining: A Tutorial-Based Primer, Second Edition, Edition 2

    Data Mining: A Tutorial-Based Primer, Second Edition, Edition 2
    Richard J. Roiger

    Data Mining: A Tutorial-Based Primer, Second Edition provides a comprehensive introduction to data mining with a focus on model building and testing, as well as on interpreting and validating results. The text guides students to understand how data mining can be employed to solve real problems and recognize whether a data mining solution is a feasible alternative for a specific problem. Fundamental data mining strategies, techniques, and evaluation methods are presented and implemented with the help of two well-known software tools. Several new topics have been added to the second edition including an introduction to Big Data and data analytics, ROC curves, Pareto lift charts, methods for handling large-sized, streaming and imbalanced data, support vector machines, and extended coverage of textual data mining. The second edition contains tutorials for attribute selection, dealing with imbalanced data, outlier analysis, time series analysis, mining textual data, and more. The text provides in-depth coverage of RapidMiner Studio and Weka’s Explorer interface. Both software tools are used for stepping students through the tutorials depicting the knowledge discovery process. This allows the reader maximum flexibility for their hands-on data mining experience.

  • Salesforce CRM: The Definitive Admin Handbook

    Salesforce CRM: The Definitive Admin Handbook
    Paul Goodey

    A practical guide which will help to discover how to setup and configure the Salesforce CRM application. It offers solutions and practical examples on how to further improve and maintain its functionality with clear systematic instructions. Being highly organized and compact, this book contains detailed instructions with screenshots, diagrams, and tips that clearly describe how you can administer and configure complex Salesforce CRM functionality with absolute ease.This book is for administrators who want to develop and strengthen their Salesforce CRM skills in the areas of configuration and system management. Whether you are a novice or a more experienced admin, this book aims to enhance your knowledge and understanding of the Salesforce CRM platform and by the end of the book, you should be ready to administer Salesforce CRM in a real-world environment.

  • Human + Machine: Reimagining Work in the Age of AI

    Human + Machine: Reimagining Work in the Age of AI
    Paul R. Daugherty

    AI is radically transforming business. Are you ready?Look around you. Artificial intelligence is no longer just a futuristic notion. It's here right now–in software that senses what we need, supply chains that "think" in real time, and robots that respond to changes in their environment. Twenty-first-century pioneer companies are already using AI to innovate and grow fast. The bottom line is this: Businesses that understand how to harness AI can surge ahead. Those that neglect it will fall behind. Which side are you on?In Human + Machine, Accenture leaders Paul R. Daugherty and H. James (Jim) Wilson show that the essence of the AI paradigm shift is the transformation of all business processes within an organization–whether related to breakthrough innovation, everyday customer service, or personal productivity habits. As humans and smart machines collaborate ever more closely, work processes become more fluid and adaptive, enabling companies to change them on the fly–or to completely reimagine them. AI is changing all the rules of how companies operate.Based on the authors' experience and research with 1,500 organizations, the book reveals how companies are using the new rules of AI to leap ahead on innovation and profitability, as well as what you can do to achieve similar results. It describes six entirely new types of hybrid human + machine roles that every company must develop, and it includes a "leader’s guide" with the five crucial principles required to become an AI-fueled business.Human + Machine provides the missing and much-needed management playbook for success in our new age of AI.BOOK PROCEEDS FOR THE AI GENERATIONThe authors' goal in publishing Human + Machine is to help executives, workers, students and others navigate the changes that AI is making to business and the economy. They believe AI will bring innovations that truly improve the way the world works and lives. However, AI will cause disruption, and many people will need education, training and support to prepare for the newly created jobs. To support this need, the authors are donating the royalties received from the sale of this book to fund education and retraining programs focused on developing fusion skills for the age of artificial intelligence.

  • The Parametric Lambda Calculus: A Metamodel for Computation

    The Parametric Lambda Calculus: A Metamodel for Computation
    Simona Ronchi Della Rocca

    The book contains a completely new presentation of classical results in the field of Lambda Calculus, together with new results. The text is unique in that it presents a new calculus (Parametric Lambda Calculus) which can be instantiated to obtain already known lambda-calculi. Some properties, which in the literature have been proved separately for different calculi, can be proved once for the Parametric one. The lambda calculi are presented from a Computer Science point of view, with a particular emphasis on their semantics, both operational and denotational.

  • Big Data and Social Science: A Practical Guide to Methods and Tools

    Big Data and Social Science: A Practical Guide to Methods and Tools
    Ian Foster

    Both Traditional Students and Working Professionals Acquire the Skills to Analyze Social Problems. Big Data and Social Science: A Practical Guide to Methods and Tools shows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This concrete approach is illustrated throughout using an important national problem, the quantitative study of innovation. The text draws on the expertise of prominent leaders in statistics, the social sciences, data science, and computer science to teach students how to use modern social science research principles as well as the best analytical and computational tools. It uses a real-world challenge to introduce how these tools are used to identify and capture appropriate data, apply data science models and tools to that data, and recognize and respond to data errors and limitations. For more information, including sample chapters and news, please visit the author's website.

  • Inductive Logic Programming: 23rd International Conference, ILP 2013, Rio de Janeiro, Brazil, August 28-30, 2013, Revised Selected Papers

    Inductive Logic Programming: 23rd International Conference, ILP 2013, Rio de Janeiro, Brazil, August 28-30, 2013, Revised Selected Papers
    Gerson Zaverucha

    This book constitutes the thoroughly refereed post-proceedings of the 23rd International Conference on Inductive Logic Programming, ILP 2013, held in Rio de Janeiro, Brazil, in August 2013. The 9 revised extended papers were carefully reviewed and selected from 42 submissions. The conference now focuses on all aspects of learning in logic, multi-relational learning and data mining, statistical relational learning, graph and tree mining, relational reinforcement learning, and other forms of learning from structured data.

  • Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play

    Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play
    David Foster

    Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models.Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative.Discover how variational autoencoders can change facial expressions in photosBuild practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generationCreate recurrent generative models for text generation and learn how to improve the models using attentionUnderstand how generative models can help agents to accomplish tasks within a reinforcement learning settingExplore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN

  • Machine Learning with R

    Machine Learning with R
    Brett Lantz

    Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.

  • Vsphere High Performance Cookbook

    Vsphere High Performance Cookbook
    Prasenjit Sarkar

    vSphere High Performance Cookbook is written in a practical, helpful style with numerous recipes focusing on answering and providing solutions to common, and not-so common, performance issues and problems.The book is primarily written for technical professionals with system administration skills and some VMware experience who wish to learn about advanced optimization and the configuration features and functions for vSphere 5.1.

  • Python Artificial Intelligence Projects for Beginners: Get up and running with Artificial Intelligence using 8 smart and exciting AI applications

    Python Artificial Intelligence Projects for Beginners: Get up and running with Artificial Intelligence using 8 smart and exciting AI applications
    Dr. Joshua Eckroth

    Build smart applications by implementing real-world artificial intelligence projectsKey FeaturesExplore a variety of AI projects with PythonGet well-versed with different types of neural networks and popular deep learning algorithmsLeverage popular Python deep learning libraries for your AI projectsBook DescriptionArtificial Intelligence (AI) is the newest technology that’s being employed among varied businesses, industries, and sectors. Python Artificial Intelligence Projects for Beginners demonstrates AI projects in Python, covering modern techniques that make up the world of Artificial Intelligence.This book begins with helping you to build your first prediction model using the popular Python library, scikit-learn. You will understand how to build a classifier using an effective machine learning technique, random forest, and decision trees. With exciting projects on predicting bird species, analyzing student performance data, song genre identification, and spam detection, you will learn the fundamentals and various algorithms and techniques that foster the development of these smart applications. In the concluding chapters, you will also understand deep learning and neural network mechanisms through these projects with the help of the Keras library.By the end of this book, you will be confident in building your own AI projects with Python and be ready to take on more advanced projects as you progressWhat you will learnBuild a prediction model using decision trees and random forestUse neural networks, decision trees, and random forests for classificationDetect YouTube comment spam with a bag-of-words and random forestsIdentify handwritten mathematical symbols with convolutional neural networksRevise the bird species identifier to use imagesLearn to detect positive and negative sentiment in user reviewsWho this book is forPython Artificial Intelligence Projects for Beginners is for Python developers who want to take their first step into the world of Artificial Intelligence using easy-to-follow projects. Basic working knowledge of Python programming is expected so that you’re able to play around with code

  • Oracle Data Guard 11gR2 Administration Beginner s Guide

    Oracle Data Guard 11gR2 Administration Beginner’s Guide
    Emre Baransel

    Using real-world examples and hands-on tasks, Oracle Data Guard 11gR2 Administration Beginner's Guide will give you a solid foundation in Oracle Data Guard. It has been designed to teach you everything you need to know to successfully create and operate Data Guard environments with maximum flexibility, compatibility, and effectiveness.If you are an Oracle database administrator who wants to configure and administer Data Guard configurations, then "Oracle Data Guard 11gR2 Administration Beginner's Guide" is for you. With a basic understanding of Oracle database administration, you'll be able to easily follow the book.

  • Learning Modernizr

    Learning Modernizr
    Adam Watson

    Written in an engaging, easy-to-follow style, "Learning HTML5 Modernizr" is a practical guide for using the feature detection features of HTML5 Modernizr to create forward compatible sites. "Learning HTML5 Modernizr" is great for developers looking for a broad range of use cases for feature detection. It is particularly meant for web developers who want to take advantage of the cool new HTML5 and CSS5 features but at the same time deliver a design that is not only backward, but forward compatible.

  • Python Machine Learning By Example: Implement machine learning algorithms and techniques to build intelligent systems, 2nd Edition, Edition 2

    Python Machine Learning By Example: Implement machine learning algorithms and techniques to build intelligent systems, 2nd Edition, Edition 2
    Yuxi (Hayden) Liu

    Grasp machine learning concepts, techniques, and algorithms with the help of real-world examples using Python libraries such as TensorFlow and scikit-learnKey FeaturesExploit the power of Python to explore the world of data mining and data analyticsDiscover machine learning algorithms to solve complex challenges faced by data scientists todayUse Python libraries such as TensorFlow and Keras to create smart cognitive actions for your projectsBook DescriptionThe surge in interest in machine learning (ML) is due to the fact that it revolutionizes automation by learning patterns in data and using them to make predictions and decisions. If you’re interested in ML, this book will serve as your entry point to ML.Python Machine Learning By Example begins with an introduction to important ML concepts and implementations using Python libraries. Each chapter of the book walks you through an industry adopted application. You’ll implement ML techniques in areas such as exploratory data analysis, feature engineering, and natural language processing (NLP) in a clear and easy-to-follow way.With the help of this extended and updated edition, you’ll understand how to tackle data-driven problems and implement your solutions with the powerful yet simple Python language and popular Python packages and tools such as TensorFlow, scikit-learn, gensim, and Keras. To aid your understanding of popular ML algorithms, the book covers interesting and easy-to-follow examples such as news topic modeling and classification, spam email detection, stock price forecasting, and more.By the end of the book, you’ll have put together a broad picture of the ML ecosystem and will be well-versed with the best practices of applying ML techniques to make the most out of new opportunities.What you will learnUnderstand the important concepts in machine learning and data scienceUse Python to explore the world of data mining and analyticsScale up model training using varied data complexities with Apache SparkDelve deep into text and NLP using Python libraries such NLTK and gensimSelect and build an ML model and evaluate and optimize its performanceImplement ML algorithms from scratch in Python, TensorFlow, and scikit-learnWho this book is forIf you’re a machine learning aspirant, data analyst, or data engineer highly passionate about machine learning and want to begin working on ML assignments, this book is for you. Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial although not necessary.

  • Mastering Machine Learning with R

    Mastering Machine Learning with R
    Cory Lesmeister

    Master machine learning techniques with R to deliver insights for complex projectsAbout This BookGet to grips with the application of Machine Learning methods using an extensive set of R packagesUnderstand the benefits and potential pitfalls of using machine learning methodsImplement the numerous powerful features offered by R with this comprehensive guide to building an independent R-based ML systemWho This Book Is ForIf you want to learn how to use R's machine learning capabilities to solve complex business problems, then this book is for you. Some experience with R and a working knowledge of basic statistical or machine learning will prove helpful.What You Will LearnGain deep insights to learn the applications of machine learning tools to the industryManipulate data in R efficiently to prepare it for analysisMaster the skill of recognizing techniques for effective visualization of dataUnderstand why and how to create test and training data sets for analysisFamiliarize yourself with fundamental learning methods such as linear and logistic regressionComprehend advanced learning methods such as support vector machinesRealize why and how to apply unsupervised learning methodsIn DetailMachine learning is a field of Artificial Intelligence to build systems that learn from data. Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning to your data.The book starts with introduction to Cross-Industry Standard Process for Data Mining. It takes you through Multivariate Regression in detail. Moving on, you will also address Classification and Regression trees. You will learn a couple of “Unsupervised techniques”. Finally, the book will walk you through text analysis and time series.The book will deliver practical and real-world solutions to problems and variety of tasks such as complex recommendation systems. By the end of this book, you will gain expertise in performing R machine learning and will be able to build complex ML projects using R and its packages.Style and approachThis is a book explains complicated concepts with easy to follow theory and real-world, practical applications. It demonstrates the power of R and machine learning extensively while highlighting the constraints.

  • Microprocessors & their Operating Systems: A Comprehensive Guide to 8, 16 & 32 Bit Hardware, Assembly Language & Computer Architecture

    Microprocessors & their Operating Systems: A Comprehensive Guide to 8, 16 & 32 Bit Hardware, Assembly Language & Computer Architecture
    R. C. Holland

    Provides a comprehensive guide to all of the major microprocessor families (8, 16 and 32 bit). The hardware aspects and software implications are described, giving the reader an overall understanding of microcomputer architectures. The internal processor operation of each microprocessor device is presented, followed by descriptions of the instruction set and applications for the device. Software considerations are expanded with descriptions and examples of the main high level programming languages (BASIC, Pascal and C). The book also includes detailed descriptions of the three main operating systems (CP/M, DOS and UNIX) common to the most modern personal computers.

  • Signalr: Real-Time Application Development

    Signalr: Real-Time Application Development
    Einar Ingebrigtsen

    This step-by-step guide gives you practical advice, tips, and tricks that will have you writing real-time apps quickly and easily.If you are a .NET developer who wants to be at the cutting edge of development, then this book is for you. Real-time application development is made simple in this guide, so as long as you have basic knowledge of .NET, a copy of Visual Studio, and NuGet installed, you are ready to go

  • Scala for Machine Learning

    Scala for Machine Learning
    Patrick R. Nicolas

    Are you curious about AI? All you need is a good understanding of the Scala programming language, a basic knowledge of statistics, a keen interest in Big Data processing, and this book!

  • Advances in Financial Machine Learning

    Advances in Financial Machine Learning
    Marcos Lopez de Prado

    Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

  • Computer Organization and Design: The Hardware / Software Interface

    Computer Organization and Design: The Hardware / Software Interface
    John L. Hennessy

    Computer Organization and Design: The Hardware/Software Interface presents the interaction between hardware and software at a variety of levels, which offers a framework for understanding the fundamentals of computing. This book focuses on the concepts that are the basis for computers.Organized into nine chapters, this book begins with an overview of the computer revolution. This text then explains the concepts and algorithms used in modern computer arithmetic. Other chapters consider the abstractions and concepts in memory hierarchies by starting with the simplest possible cache. This book discusses as well the complete data path and control for a processor. The final chapter deals with the exploitation of parallel machines.This book is a valuable resource for students in computer science and engineering. Readers with backgrounds in assembly language and logic design who want to learn how to design a computer or understand how a system works will also find this book useful.

  • Stabilization, Safety, and Security of Distributed Systems: 12th International Symposium, SSS 2010, New York, NY, USA, September 20-22, 2010, Proceedings

    Stabilization, Safety, and Security of Distributed Systems: 12th International Symposium, SSS 2010, New York, NY, USA, September 20-22, 2010, Proceedings
    Shlomi Dolev

    The papers in this volume were presented at the 12th International Sym- sium on Stabilization, Safety, and Security of Distributed Systems (SSS), held September 20–22, 2010 at Columbia University, NYC, USA. The SSS symposium is an international forum for researchersand practiti- ers in the design and development of distributed systems with self-* properties: (theclassical)self-stabilizing,self-con?guring,self-organizing,self-managing,se- repairing,self-healing,self-optimizing,self-adaptive,andself-protecting. Research in distributed systems is now at a crucial point in its evolution, marked by the importance of dynamic systems such as peer-to-peer networks, large-scale wi- lesssensornetworks,mobileadhocnetworks,cloudcomputing,roboticnetworks, etc. Moreover, new applications such as grid and web services, banking and- commerce, e-health and robotics, aerospaceand avionics, automotive, industrial process control, etc. , have joined the traditional applications of distributed s- tems. SSS started as the Workshop on Self-Stabilizing Systems (WSS), the ?rst two of which were held in Austin in 1989 and in Las Vegas in 1995. Starting in 1995, the workshop began to be held biennially; it was held in Santa Barbara (1997), Austin (1999), and Lisbon (2001). As interest grew and the community expanded, the title of the forum was changed in 2003 to the Symposium on Self- Stabilizing Systems (SSS). SSS was organized in San Francisco in 2003 and in Barcelona in 2005. As SSS broadened its scope and attracted researchers from other communities, a couple of changes were made in 2006. It became an – nual event, and the name of the conference was changed to the International Symposium on Stabilization, Safety, and Security of Distributed Systems (SSS).

  • Apache CloudStack Cloud Computing

    Apache CloudStack Cloud Computing
    Navin Sabharwal

    This book is packed with practical, hands-on illustrations for building and managing your CloudStack environment.If you are a cloud architect, cloud administrator, virtualization administrator, cloud storage administrator, cloud computing professional, or technical evangelist who is looking to learn and leverage CloudStack, then this book is for you. You will learn how to set up a cloud service for your enterprise or for your customer.

  • Clojure for Machine Learning

    Clojure for Machine Learning
    Akhil Wali

    A book that brings out the strengths of Clojure programming that have to facilitate machine learning. Each topic is described in substantial detail, and examples and libraries in Clojure are also demonstrated. This book is intended for Clojure developers who want to explore the area of machine learning. Basic understanding of the Clojure programming language is required, but thorough acquaintance with the standard Clojure library or any libraries are not required. Familiarity with theoretical concepts and notation of mathematics and statistics would be an added advantage.

  • 가장 빨리 만나는 딥러닝 with Caffe: 인공지능, 기계 학습, 이제는 딥러닝이다

    가장 빨리 만나는 딥러닝 with Caffe: 인공지능, 기계 학습, 이제는 딥러닝이다
    다케이 히로마사

    Deep Learning, 누구나 쉽게 사용할 수 있다!왜 딥러닝에 주목해야 하는가?딥러닝은 MIT가 선정한 10대 혁신 기술이며, IT 리서치 기업 가트너도 주목해야 할 기술로 선정했다. 구글, 애플, 마이크로소프트, IBM, 삼성전자 같은 글로벌 기업은 모두 딥러닝에 투자하고 있다. 이들은 자사 서비스를 통해 구축된 빅데이터를 활용하기 위해 노력하고 있다. 자동 음성 인식, 자율 주행차, 주가 예측, 기사 작성 등 다양한 분야에 딥러닝이 이미 쓰이고 있으며 기업 경쟁력의 핵심이 될 것으로 예측되고 있다.딥러닝, 기초부터 차근차근 이해하자!기초편에서는 딥러닝의 개요와 역사를 살펴보고, 음성 인식과 이미지 인식 분야에서 현재까지 이뤄낸 성과를 알아본다. 이론편에서는 컴퓨터가 딥러닝 알고리즘을 사용해 학습하는 방법을 배우고, 기존 방법과 비교하여 딥러닝이 왜 높은 성능을 구현할 수 있는지 살펴본다. 기계 학습이나 딥러닝에 대한 지식이 없고 수학을 몰라도 이해할 수 있도록 쉬운 용어와 구체적인 사례로 설명한다.Caffe로 딥러닝을 경험해보자!오픈 소스 딥러닝 프레임워크 Caffe를 이용하면 딥러닝을 쉽게 사용할 수 있다. Caffe를 설치하고, 파라미터를 설정하고, 실제로 실행해보면서 딥러닝을 경험해보자. 리눅스 사용자는 CUDA와 함께 GPU 환경에서, 윈도 사용자는 VMware에 리눅스를 설치하고 CPU 환경에서 Caffe를 사용해본다.

  • MATLAB for Machine Learning

    MATLAB for Machine Learning
    Giuseppe Ciaburro

    Extract patterns and knowledge from your data in easy way using MATLABAbout This BookGet your first steps into machine learning with the help of this easy-to-follow guideLearn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLABUnderstand how your data works and identify hidden layers in the data with the power of machine learning.Who This Book Is ForThis book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well.What You Will LearnLearn the introductory concepts of machine learning.Discover different ways to transform data using SAS XPORT, import and export tools,Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data.Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment.Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures.Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox.Learn feature selection and extraction for dimensionality reduction leading to improved performance.In DetailMATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners.You'll start by getting your system ready with t he MATLAB environment for machine learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions.You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement.At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB.Style and approachThe book takes a very comprehensive approach to enhance your understanding of machine learning using MATLAB. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work.

  • A Concise Introduction to Models and Methods for Automated Planning

    A Concise Introduction to Models and Methods for Automated Planning
    Hector Geffner

    Planning is the model-based approach to autonomous behavior where the agent behavior is derived automatically from a model of the actions, sensors, and goals. The main challenges in planning are computational as all models, whether featuring uncertainty and feedback or not, are intractable in the worst case when represented in compact form. In this book, we look at a variety of models used in AI planning, and at the methods that have been developed for solving them. The goal is to provide a modern and coherent view of planning that is precise, concise, and mostly self-contained, without being shallow. For this, we make no attempt at covering the whole variety of planning approaches, ideas, and applications, and focus on the essentials. The target audience of the book are students and researchers interested in autonomous behavior and planning from an AI, engineering, or cognitive science perspective. Table of Contents: Preface / Planning and Autonomous Behavior / Classical Planning: Full Information and Deterministic Actions / Classical Planning: Variations and Extensions / Beyond Classical Planning: Transformations / Planning with Sensing: Logical Models / MDP Planning: Stochastic Actions and Full Feedback / POMDP Planning: Stochastic Actions and Partial Feedback / Discussion / Bibliography / Author's Biography

  • Learning Robotics Using Python

    Learning Robotics Using Python
    Lentin Joseph

    If you are an engineer, a researcher, or a hobbyist, and you are interested in robotics and want to build your own robot, this book is for you. Readers are assumed to be new to robotics but should have experience with Python.

  • The Quick Guide to Robotics and Artificial Intelligence: Surviving the Automation Revolution for Beginners

    The Quick Guide to Robotics and Artificial Intelligence: Surviving the Automation Revolution for Beginners
    Alex Nkenchor Uwajeh

    Please note: ***This is a Beginner's Basic Guide to Robotics, Artificial Intelligence and Automation***Technology has advanced significantly since inception, allowing developers and researchers to integrate AI programming and robotics into things you probably already use in your daily life. Our entire society is at a major turning point in terms of how we think about work, career advancement, and income-earning potential. As technology continues to expand and grow, the sheer number of people who will be displaced and made redundant in their current occupations is increasingly likely.

  • Biometric Technology: Authentication, Biocryptography, and Cloud-Based Architecture

    Biometric Technology: Authentication, Biocryptography, and Cloud-Based Architecture
    Ravi Das

    Most biometric books are either extraordinarily technical for technophiles or extremely elementary for the lay person. Striking a balance between the two, Biometric Technology: Authentication, Biocryptography, and Cloud-Based Architecture is ideal for business, IT, or security managers that are faced with the task of making purchasing, migration, or adoption decisions. It brings biometrics down to an understandable level, so that you can immediately begin to implement the concepts discussed.Exploring the technological and social implications of widespread biometric use, the book considers the science and technology behind biometrics as well as how it can be made more affordable for small and medium-sized business. It also presents the results of recent research on how the principles of cryptography can make biometrics more secure. Covering biometric technologies in the cloud, including security and privacy concerns, the book includes a chapter that serves as a "how-to manual" on procuring and deploying any type of biometric system. It also includes specific examples and case studies of actual biometric deployments of localized and national implementations in the U.S. and other countries.The book provides readers with a technical background on the various biometric technologies and how they work. Examining optimal application in various settings and their respective strengths and weaknesses, it considers ease of use, false positives and negatives, and privacy and security issues. It also covers emerging applications such as biocryptography.Although the text can be understood by just about anybody, it is an ideal resource for corporate-level executives who are considering implementing biometric technologies in their organizations.

  • Machine Learning with Spark

    Machine Learning with Spark
    Nick Pentreath

    If you are a Scala, Java, or Python developer with an interest in machine learning and data analysis and are eager to learn how to apply common machine learning techniques at scale using the Spark framework, this is the book for you. While it may be useful to have a basic understanding of Spark, no previous experience is required.

  • Automata and Computability

    Automata and Computability
    Dexter C. Kozen

    These are my lecture notes from CS381/481: Automata and Computability Theory, a one-semester senior-level course I have taught at Cornell Uni versity for many years. I took this course myself in thc fall of 1974 as a first-year Ph.D. student at Cornell from Juris Hartmanis and have been in love with the subject ever sin,:e. The course is required for computer science majors at Cornell. It exists in two forms: CS481, an honors version; and CS381, a somewhat gentler paced version. The syllabus is roughly the same, but CS481 go es deeper into thc subject, covers more material, and is taught at a more abstract level. Students are encouraged to start off in one or the other, then switch within the first few weeks if they find the other version more suitaLle to their level of mathematical skill. The purpose of t.hc course is twofold: to introduce computer science students to the rieh heritage of models and abstractions that have arisen over the years; and to dew!c'p the capacity to form abstractions of their own and reason in terms of them.

  • Advances in Financial Machine Learning

    Advances in Financial Machine Learning
    Marcos Lopez de Prado

    Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

  • Bayesian Networks: With Examples in R

    Bayesian Networks: With Examples in R
    Marco Scutari

    Understand the Foundations of Bayesian Networks—Core Properties and Definitions Explained Bayesian Networks: With Examples in R introduces Bayesian networks using a hands-on approach. Simple yet meaningful examples in R illustrate each step of the modeling process. The examples start from the simplest notions and gradually increase in complexity. The authors also distinguish the probabilistic models from their estimation with data sets. The first three chapters explain the whole process of Bayesian network modeling, from structure learning to parameter learning to inference. These chapters cover discrete Bayesian, Gaussian Bayesian, and hybrid networks, including arbitrary random variables. The book then gives a concise but rigorous treatment of the fundamentals of Bayesian networks and offers an introduction to causal Bayesian networks. It also presents an overview of R and other software packages appropriate for Bayesian networks. The final chapter evaluates two real-world examples: a landmark causal protein signaling network paper and graphical modeling approaches for predicting the composition of different body parts. Suitable for graduate students and non-statisticians, this text provides an introductory overview of Bayesian networks. It gives readers a clear, practical understanding of the general approach and steps involved.

  • Performance Testing With JMeter 2.9

    Performance Testing With JMeter 2.9
    Bayo Erinle

    Performance Testing With JMeter 2.9 is a standard tutorial that will help you polish your fundamentals, guide you through various advanced topics, and along the process help you learn new tools and skills.This book is for developers, quality assurance engineers, testers, and test managers new to Apache JMeter, or those who are looking to get a good grounding in how to effectively use and become proficient with it. No prior testing experience is required.