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Books
Artificial Intelligence
Exploring Intelligent Decision Support Systems: Current State and New Trends (Studies in Computational Intelligence)
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This book presents innovative and highquality research regarding advanced decision support systems (DSSs). It describes the foundations, methods, methodologies, models, tools, and techniques for designing, developing, implementing and evaluating advanced DSSs in different fields, including finance, health, emergency management, industry and pollution control. Decision support systems employ artificial intelligence methods to heuristically address problems that are cannot be solved using formal techniques. In this context, technologies such as the Semantic Web, linked data, big data, and machine learning are being applied to provide integrated support for individuals and organizations to make more rational decisions. The book is organized into two parts. The first part covers decision support systems for industry, while the second part presents case studies related to clinical emergency management and pollution control.


Data Smart: Using Data Science to Transform Information into Insight
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Author: John W. Foreman
Brand: John Wiley Sons

Data Science gets thrown around in the press like it's magic. 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 straightforward 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.
Features:
 Data Science gets thrown around in the press like it's magic. 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.
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Concrete Mathematics: A Foundation for Computer Science (2nd Edition)
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Author: Ronald L. Graham
Brand: Graham, Ronald L.

This book introduces the mathematics that supports advanced computer programming and the analysis of algorithms. The primary aim of its wellknown authors is to provide a solid and relevant base of mathematical skills  the skills needed to solve complex problems, to evaluate horrendous sums, and to discover subtle patterns in data. It is an indispensable text and reference not only for computer scientists  the authors themselves rely heavily on it!  but for serious users of mathematics in virtually every discipline. Concrete Mathematics is a blending of CONtinuous and disCRETE mathematics. "More concretely," the authors explain, "it is the controlled manipulation of mathematical formulas, using a collection of techniques for solving problems." The subject matter is primarily an expansion of the Mathematical Preliminaries section in Knuth's classic Art of Computer Programming, but the style of presentation is more leisurely, and individual topics are covered more deeply. Several new topics have been added, and the most significant ideas have been traced to their historical roots. The book includes more than 500 exercises, divided into six categories. Complete answers are provided for all exercises, except research problems, making the book particularly valuable for selfstudy. Major topics include:  Sums
 Recurrences
 Integer functions
 Elementary number theory
 Binomial coefficients
 Generating functions
 Discrete probability
 Asymptotic methods
This second edition includes important new material about mechanical summation. In response to the widespread use of the first edition as a reference book, the bibliography and index have also been expanded, and additional nontrivial improvements can be found on almost every page. Readers will appreciate the informal style of Concrete Mathematics. Particularly enjoyable are the marginal graffiti contributed by students who have taken courses based on this material. The authors want to convey not only the importance of the techniques presented, but some of the fun in learning and using them.
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AIQ: How People and Machines Are Smarter Together
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Author: Nick Polson

“There comes a time in the life of a subject when someone steps up and writes the book about it. AIQ explores the fascinating history of the ideas that drive this technology of the future and demystifies the core concepts behind it; the result is a positive and entertaining look at the great potential unlocked by marrying human creativity with powerful machines.” ―Steven D. Levitt, bestselling coauthor of Freakonomics From leading data scientists Nick Polson and James Scott, what everyone needs to know to understand how artificial intelligence is changing the world and how we can use this knowledge to make better decisions in our own lives. Dozens of times per day, we all interact with intelligent machines that are constantly learning from the wealth of data now available to them. These machines, from smart phones to talking robots to selfdriving cars, are remaking the world in the 21st century in the same way that the Industrial Revolution remade the world in the 19th century. AIQ is based on a simple premise: if you want to understand the modern world, then you have to know a little bit of the mathematical language spoken by intelligent machines. AIQ will teach you that language―but in an unconventional way, anchored in stories rather than equations. You will meet a fascinating cast of historical characters who have a lot to teach you about data, probability, and better thinking. Along the way, you'll see how these same ideas are playing out in the modern age of big data and intelligent machines―and how these technologies will soon help you to overcome some of your builtin cognitive weaknesses, giving you a chance to lead a happier, healthier, more fulfilled life.
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The Sentient Machine: The Coming Age of Artificial Intelligence
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Author: Amir Husain

The future is now. Acclaimed technologist and inventor Amir Husain explains how we can live amidst the coming age of sentient machines and artificial intelligence—and not only survive, but thrive.
Artificial “machine” intelligence is playing an evergreater role in our society. We are already using cruise control in our cars, automatic checkout at the drugstore, and are unable to live without our smartphones. The discussion around AI is polarized; people think either machines will solve all problems for everyone, or they will lead us down a dark, dystopian path into total human irrelevance. Regardless of what you believe, the idea that we might bring forth intelligent creation can be intrinsically frightening. But what if our greatest role as humans so far is that of creators? Amir Husain, a brilliant inventor and computer scientist, argues that we are on the cusp of writing our next, and greatest, creation myth. It is the dawn of a new form of intellectual diversity, one that we need to embrace in order to advance the state of the art in many critical fields, including security, resource management, finance, and energy. “In The Sentient Machine, Husain prepares us for a brighter future; not with hyperbole about right and wrong, but with serious arguments about risk and potential” (Dr. Greg Hyslop, Chief Technology Officer, The Boeing Company). He addresses broad existential questions surrounding the coming of AI: Why are we valuable? What can we create in this world? How are we intelligent? What constitutes progress for us? And how might we fail to progress? Husain boils down complex computer science and AI concepts into clear, plainspoken language and draws from a wide variety of cultural and historical references to illustrate his points. Ultimately, Husain challenges many of our societal norms and upends assumptions we hold about “the good life.”
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Machine Learning with R  Second Edition: Expert techniques for predictive modeling to solve all your data analysis problems
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Author: Brett Lantz
Brand: Lantz Brett

Key Features Harness the power of R for statistical computing and data science
 Explore, forecast, and classify data with R
 Use R to apply common machine learning algorithms to realworld scenarios
Book DescriptionMachine learning, at its core, is concerned with transforming data into actionable knowledge. This makes machine learning well suited to the presentday era of big data. Given the growing prominence of R's crossplatform, zerocost statistical programming environment, there has never been a better time to start applying machine learning to your data. Whether you are new to data analytics or a veteran, machine learning with R offers a powerful set of methods to quickly and easily gain insights from your data. Want to turn your data into actionable knowledge, predict outcomes that make real impact, and have constantly developing insights? R gives you access to the cuttingedge power you need to master exceptional machine learning techniques. Updated and upgraded to the latest libraries and most modern thinking, the second edition of Machine Learning with R provides you with a rigorous introduction to this essential skill of professional data science. Without shying away from technical theory, it is written to provide focused and practical knowledge to get you building algorithms and crunching your data, with minimal previous experience. With this book you'll discover all the analytical tools you need to gain insights from complex data and learn how to to choose the correct algorithm for your specific needs. Through full engagement with the sort of realworld problems datawranglers face, you'll learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, market analysis, and clustering. Transform the way you think about data; discover machine learning with R. What you will learn Harness the power of R to build common machine learning algorithms with realworld data science applications
 Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results
 Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems
 Classify your data with Bayesian and nearest neighbour methods
 Predict values by using R to build decision trees, rules, and support vector machines
 Forecast numeric values with linear regression, and model your data with neural networks
 Evaluate and improve the performance of machine learning models
 Learn specialized machine learning techniques for text mining, social network data, big data, and more
About the AuthorBrett Lantz has used innovative data methods to understand human behavior for more than 10 years. A sociologist by training, he was first enchanted by machine learning while studying a large database of teenagers' social networking website profiles. Since then, he has worked on the interdisciplinary studies of cellular telephone calls, medical billing data, and philanthropic activity, among others. Table of Contents Introducing Machine Learning
 Managing and Understanding Data
 Lazy Learning  Classification Using Nearest Neighbors
 Probabilistic Learning  Classification Using Naive Bayes
 Divide and Conquer  Classification Using Decision Trees and Rules
 Forecasting Numeric Data  Regression Methods
 Black Box Methods  Neural Networks and Support Vector Machines
 Finding Patterns  Market Basket Analysis Using Association Rules
 Finding Groups of Data  Clustering with Kmeans
 Evaluating Model Performance
 Improving Model Performance
Features:
 Machine Learning with R Second Edition
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 R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
 An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
 R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics (O'reilly Cookbooks)
 Text Mining with R: A Tidy Approach
 Applied Predictive Modeling
 ggplot2: Elegant Graphics for Data Analysis (Use R!)
 Deep Learning with R
 Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press)
 HandsOn Machine Learning with ScikitLearn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
 Practical Statistics for Data Scientists: 50 Essential Concepts


Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit
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List price: $44.99
Author: Steven Bird
Brand: Brand: O'Reilly Media
Model: 14055174

This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.
Packed with examples and exercises, Natural Language Processing with Python will help you:
 Extract information from unstructured text, either to guess the topic or identify "named entities"
 Analyze linguistic structure in text, including parsing and semantic analysis
 Access popular linguistic databases, including WordNet and treebanks
 Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence
This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages  or if you're simply curious to have a programmer's perspective on how human language works  you'll find Natural Language Processing with Python both fascinating and immensely useful.
Features:
 Used Book in Good Condition
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Make Your Own Neural Network: An Indepth Visual Introduction For Beginners
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List price: $9.99
Author: Michael Taylor
Brand: Independently published

A stepbystep visual journey through the mathematics of neural networks, and making your own using Python and Tensorflow. What you will gain from this book: * A deep understanding of how a Neural Network works. * How to build a Neural Network from scratch using Python. Who this book is for: * Beginners who want to fully understand how networks work, and learn to build two stepbystep examples in Python. * Programmers who need an easy to read, but solid refresher, on the math of neural networks. What’s Inside  ‘Make Your Own Neural Network: An Indepth Visual Introduction For Beginners’ What Is a Neural Network? Neural networks have made a gigantic comeback in the last few decades and you likely make use of them everyday without realizing it, but what exactly is a neural network? What is it used for and how does it fit within the broader arena of machine learning? we gently explore these topics so that we can be prepared to dive deep further on. To start, we’ll begin with a highlevel overview of machine learning and then drill down into the specifics of a neural network. The Math of Neural Networks On a high level, a network learns just like we do, through trial and error. This is true regardless if the network is supervised, unsupervised, or semisupervised. Once we dig a bit deeper though, we discover that a handful of mathematical functions play a major role in the trial and error process. It also becomes clear that a grasp of the underlying mathematics helps clarify how a network learns. * Forward Propagation * Calculating The Total Error * Calculating The Gradients * Updating The Weights Make Your Own Artificial Neural Network: Hands on Example You will learn to build a simple neural network using all the concepts and functions we learned in the previous few chapters. Our example will be basic but hopefully very intuitive. Many examples available online are either hopelessly abstract or make use of the same data sets, which can be repetitive. Our goal is to be crystal clear and engaging, but with a touch of fun and uniqueness. This section contains the following eight chapters. Building Neural Networks in Python There are many ways to build a neural network and lots of tools to get the job done. This is fantastic, but it can also be overwhelming when you start, because there are so many tools to choose from. We are going to take a look at what tools are needed and help you nail down the essentials. To build a neural network Tensorflow and Neural Networks There is no single way to build a feedforward neural network with Python, and that is especially true if you throw Tensorflow into the mix. However, there is a general framework that exists that can be divided into five steps and grouped into two parts. We are going to briefly explore these five steps so that we are prepared to use them to build a network later on. Ready? Let’s begin. Neural Network: Distinguish Handwriting We are going to dig deep with Tensorflow and build a neural network that can distinguish between handwritten numbers. We’ll use the same 5 steps we covered in the highlevel overview, and we are going to take time exploring each line of code. Neural Network: Classify Images 10 minutes. That’s all it takes to build an image classifier thanks to Google! We will provide a highlevel overview of how to classify images using a convolutional neural network (CNN) and Google’s Inception V3 model. Once finished, you will be able to tweak this code to classify any type of image sets! Cats, bats, super heroes  the sky’s the limit.
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Natural Language Processing with TensorFlow: Teach language to machines using Python's deep learning library
Lowest new price: $39.99
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Author: Thushan Ganegedara

Write modern natural language processing applications using deep learning algorithms and TensorFlow Key Features Focuses on more efficient natural language processing using TensorFlow
 Covers NLP as a field in its own right to improve understanding for choosing TensorFlow tools and other deep learning approaches
 Provides choices for how to process and evaluate large unstructured text datasets
 Learn to apply the TensorFlow toolbox to specific tasks in the most interesting field in artificial intelligence
Book DescriptionNatural language processing (NLP) supplies the majority of data available to deep learning applications, while TensorFlow is the most important deep learning framework currently available. Natural Language Processing with TensorFlow brings TensorFlow and NLP together to give you invaluable tools to work with the immense volume of unstructured data in today's data streams, and apply these tools to specific NLP tasks. Thushan Ganegedara starts by giving you a grounding in NLP and TensorFlow basics. You'll then learn how to use Word2vec, including advanced extensions, to create word embeddings that turn sequences of words into vectors accessible to deep learning algorithms. Chapters on classical deep learning algorithms, like convolutional neural networks (CNN) and recurrent neural networks (RNN), demonstrate important NLP tasks as sentence classification and language generation. You will learn how to apply highperformance RNN models, like long shortterm memory (LSTM) cells, to NLP tasks. You will also explore neural machine translation and implement a neural machine translator. After reading this book, you will gain an understanding of NLP and you'll have the skills to apply TensorFlow in deep learning NLP applications, and how to perform specific NLP tasks. What you will learn Core concepts of NLP and various approaches to natural language processing
 How to solve NLP tasks by applying TensorFlow functions to create neural networks
 Strategies to process large amounts of data into word representations that can be used by deep learning applications
 Techniques for performing sentence classification and language generation using CNNs and RNNs
 About employing stateofthe art advanced RNNs, like long shortterm memory, to solve complex text generation tasks
 How to write automatic translation programs and implement an actual neural machine translator from scratch
 The trends and innovations that are paving the future in NLP
Who This Book Is ForThis book is for Python developers with a strong interest in deep learning, who want to learn how to leverage TensorFlow to simplify NLP tasks. Fundamental Python skills are assumed, as well as some knowledge of machine learning and undergraduatelevel calculus and linear algebra. No previous natural language processing experience required, although some background in NLP or computational linguistics will be helpful. Table of Contents Introduction
 How to Get TensorFlow to Work
 Producing Word Embeddings with Word2Vec
 Advanced Word2Vec
 Sentence Classification with CNNs
 Language Modelling with RNNs
 What is LSTM?
 Applying LSTM to Text Generation
 Applications of LSTM: Image Caption Generation
 Neural Machine Translation
 NLP developments and Trends
 Appendix I Linear Algebra and Statistics
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TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning
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List price: $69.99
Author: Bharath Ramsundar

Learn how to solve challenging machine learning problems with TensorFlow, Google’s revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machinelearning fundamentals by showing you how to design systems capable of detecting objects in images, understanding text, analyzing video, and predicting the properties of potential medicines. TensorFlow for Deep Learning teaches concepts through practical examples and helps you build knowledge of deep learning foundations from the ground up. It’s ideal for practicing developers with experience designing software systems, and useful for scientists and other professionals familiar with scripting but not necessarily with designing learning algorithms.  Learn TensorFlow fundamentals, including how to perform basic computation
 Build simple learning systems to understand their mathematical foundations
 Dive into fully connected deep networks used in thousands of applications
 Turn prototypes into highquality models with hyperparameter optimization
 Process images with convolutional neural networks
 Handle natural language datasets with recurrent neural networks
 Use reinforcement learning to solve games such as tictactoe
 Train deep networks with hardware including GPUs and tensor processing units
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