|Browse by Catagory:
Civil Rights & Liberties
War & Peace
Cajun & Creole Cooking
Caribbean & West Indian Cooking
Diabetic & Sugar-Free Cooking
Low Fat Cooking
Middle Eastern Cooking
Pacific Rim Cooking
Home & Garden
Literature & Fiction
Sheet Music & Scores
Environmental & Natural Resources Law
Ethics & Professional Responsibility
Procedures & Litigation
Water Supply & Land Use
Lawyer and Crimal Humor
Outdoors & Nature
Hiking & Camping
Hunting & Fishing
Beer & Beer Making
Health & Fitness
Diets & Weight Loss
Children's Science & Nature
Vitamins & Supplements
Psychology and Counseling
Philosophy of Psychology
Physiological Aspects of Psychology
Psychology of Sexuality
Psychology Testing & Measurement
Chaos & Systems
Geometry & Topology
Logic & Brain Teasers
Chaos & Systems
Geometry & Topology
Probability & Statistics
Experiments, Instruments & Measurement
Chaos & Systems
Fusion & Fission
Nuclear Magnetic Resonance
Waves & Wave Mechanics
Administration & Policy
Allied Health Professions
Medical Education & Training
Endocrinology & Metabolism
Physician & Patient
Insects & Spiders
Fish & Aquariums
Mobile & Wireless Computing: Programming
Linux Kernel & Peripherals
Linux Networking & Administration
State & Local History
Sci Fi Calendars
Bujold, Lois McMaster
Card, Orson Scott
Chalker, Jack L.
Heinlein, Robert A.
McKillip, Patricia A.
Nye, Jody Lynn
|Exploring Intelligent Decision Support Systems: Current State and New Trends (Studies in Computational Intelligence)
Lowest new price: $116.16
Lowest used price: $138.46
List price: $129.00
This book presents innovative and high-quality 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
Lowest new price: $20.88
Lowest used price: $15.90
List price: $45.00
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 straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet.
- 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.
|Concrete Mathematics: A Foundation for Computer Science (2nd Edition)
Lowest new price: $40.29
Lowest used price: $32.60
List price: $89.99
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 well-known 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 self-study.
Major topics include:
- 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.
|AIQ: How People and Machines Are Smarter Together
Lowest new price: $10.35
Lowest used price: $14.11
List price: $26.99
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 co-author 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 self-driving 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 built-in cognitive weaknesses, giving you a chance to lead a happier, healthier, more fulfilled life.
|The Sentient Machine: The Coming Age of Artificial Intelligence
Lowest new price: $8.26
Lowest used price: $7.15
List price: $27.00
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 ever-greater 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.”
|Machine Learning with R - Second Edition: Expert techniques for predictive modeling to solve all your data analysis problems
Lowest new price: $47.10
Lowest used price: $41.09
List price: $54.99
Author: Brett Lantz
Brand: Lantz Brett
- 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 real-world scenarios
Machine learning, at its core, is concerned with transforming data into actionable knowledge. This makes machine learning well suited to the present-day era of big data. Given the growing prominence of R's cross-platform, zero-cost 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 cutting-edge 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 real-world problems data-wranglers 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 real-world 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 Author
Brett 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 K-means
- Evaluating Model Performance
- Improving Model Performance
- Machine Learning with R Second Edition
- 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)
- Hands-On Machine Learning with Scikit-Learn 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
Lowest new price: $19.16
Lowest used price: $12.36
List price: $44.99
Author: Steven Bird
Brand: Brand: O'Reilly Media
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.
- Used Book in Good Condition
|Make Your Own Neural Network: An In-depth Visual Introduction For Beginners
Lowest new price: $8.71
List price: $9.99
Author: Michael Taylor
Brand: Independently published
A step-by-step 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 step-by-step 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 high-level 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 semi-supervised. 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 high-level 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 high-level 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.
|Natural Language Processing with TensorFlow: Teach language to machines using Python's deep learning library
Lowest new price: $39.99
Lowest used price: $49.58
List price: $39.99
Author: Thushan Ganegedara
Write modern natural language processing applications using deep learning algorithms and TensorFlow
- 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
Natural 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 high-performance RNN models, like long short-term 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 state-of-the art advanced RNNs, like long short-term 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 For
This 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 undergraduate-level 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
- 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
|TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning
Lowest new price: $45.69
Lowest used price: $41.00
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 machine-learning 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 high-quality 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 tic-tac-toe
- Train deep networks with hardware including GPUs and tensor processing units
Page 5 of 3116
CERTAIN CONTENT THAT APPEARS ON THIS SITE COMES FROM AMAZON SERVICES LLC. THIS CONTENT IS PROVIDED AS IS AND IS SUBJECT TO CHANGE OR REMOVAL AT ANY TIME.