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|Rise of the Robots: Technology and the Threat of a Jobless Future
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Author: Martin Ford
Brand: Martin Ford
The New York Times-bestselling guide to how automation is changing the economy, undermining work, and reshaping our lives
"Lucid, comprehensive, and unafraid...;an indispensable contribution to a long-running argument."--Los Angeles Times
Winner of the 2015 FT & McKinsey Business Book of the Year Award
Top Business Book of 2015 at Forbes
One of NBCNews.com 12 Notable Science and Technology Books of 2015
What are the jobs of the future? How many will there be? And who will have them? As technology continues to accelerate and machines begin taking care of themselves, fewer people will be necessary. Artificial intelligence is already well on its way to making "good jobs" obsolete: many paralegals, journalists, office workers, and even computer programmers are poised to be replaced by robots and smart software. As progress continues, blue and white collar jobs alike will evaporate, squeezing working- and middle-class families ever further. At the same time, households are under assault from exploding costs, especially from the two major industries-education and health care-that, so far, have not been transformed by information technology. The result could well be massive unemployment and inequality as well as the implosion of the consumer economy itself.
The past solutions to technological disruption, especially more training and education, aren't going to work. We must decide, now, whether the future will see broad-based prosperity or catastrophic levels of inequality and economic insecurity. Rise of the Robots is essential reading to understand what accelerating technology means for our economic prospects-not to mention those of our children-as well as for society as a whole.
- Rise of the Robots Technology and the Threat of a Jobless Future
- The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies
- The Industries of the Future
- The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies
- All Your Worth: The Ultimate Lifetime Money Plan
- The Future of Work: Robots, AI, and Automation
- The Two-Income Trap: Why Middle-Class Parents Are (Still) Going Broke
- A Concise Guide to Macroeconomics, Second Edition: What Managers, Executives, and Students Need to Know
- The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future
- Shaping the Fourth Industrial Revolution
- Superintelligence: Paths, Dangers, Strategies
|Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning)
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Author: Kevin P. Murphy
A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.
Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.
The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package―PMTK (probabilistic modeling toolkit)―that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
|Machine Learning For Absolute Beginners: A Plain English Introduction (Machine Learning For Beginners)
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Author: Oliver Theobald
Please note that this book is not a sequel to the First Edition, but rather a restructured and revamped version of the First Edition.
Ready to crank up a virtual server and smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile?
Well, hold on there...
Before you embark on your epic journey into the world of machine learning, there is some theory and statistical principles to march through first. But rather than spend $30-$50 USD on a dense long textbook, you may want to read this book first. As a clear and concise alternative to a textbook, this book provides a practical and high-level introduction to the practical components and statistical concepts found in machine learning. Machine Learning for Absolute Beginners Second Edition has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy and engaging to follow along at home. This major new edition features many topics not covered in the First Edition, including Cross Validation, Data Scrubbing and Ensemble Modeling. Please note that this book is not a sequel to the First Edition, but rather a restructured and revamped version of the First Edition. Readers of the First Edition should not feel compelled to purchase this Second Edition. Disclaimer: If you have passed the 'beginner' stage in your study of machine learning and are ready to tackle coding and deep learning, you would be well served with a long-format textbook. If, however, you are yet to reach that Lion King moment—as a fully grown Simba looking over the Pride Lands of Africa—then this is the book to gently hoist you up and offer you a clear lay of the land.
In this step-by-step guide you will learn: - How to download free datasets - What tools and machine learning libraries you need - Data scrubbing techniques, including one-hot encoding, binning and dealing with missing data - Preparing data for analysis, including k-fold Validation - Regression analysis to create trend lines - Clustering, including k-means and k-nearest Neighbors - The basics of Neural Networks - Bias/Variance to improve your machine learning model - Decision Trees to decode classification - How to build your first Machine Learning Model to predict house values using Python Frequently Asked Questions Q: Do I need programming experience to complete this book? A: This book is designed for absolute beginners, so no programming experience is required. However, two of the later chapters introduce Python to demonstrate an actual machine learning model, so you will see programming language used in this book. Q: I have already purchased the First Edition of this book, should I purchase this Second Edition? A: As majority of the topics from the First Edition are covered in the Second Edition, you may be better served reading a more advanced title on machine learning. Q: Can I get access to the Kindle version of this book? A: Yes. Under Amazon’s Matchbook program, the purchaser of this book can add the Kindle version of this title (valued at $3.99 USD) to their Amazon Kindle library at no cost. Q: Does this book include everything I need to become a machine learning expert? A: This book is designed for readers taking their first steps in machine learning and further learning will be required beyond this book to master machine learning.
|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 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.
|Applied Artificial Intelligence: A Handbook For Business Leaders
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Author: Mariya Yao
***Selected as a CES 2018 Top Technology Book of the Year ***
"Artificial intelligence" is the buzzword of the day. You've no doubt read your fair share of media hype either proclaiming doom and gloom where robots seize our jobs or prophesying a new utopia where AI cures all our human problems. But what does it actually mean for your role as a business leader?
Applied Artificial Intelligence is a practical guide for business leaders who are passionate about leveraging machine intelligence to enhance the productivity of their organizations and the quality of life in their communities. If you want to drive innovation by combining data, technology, design, and people to solve real problems at an enterprise scale, this is your playbook.
This book does not overload you with details on debugging TensorFlow code nor bore you with generalizations about the future of humanity. Instead, we teach you how to lead successful AI initiatives by prioritizing the right opportunities, building a diverse team of experts, conducting strategic experiments, and consciously designing your solutions to benefit both your organization and society as a whole. This book is focused on helping you drive concrete business decisions through applications of artificial intelligence and machine learning.
Written with the combined knowledge of three experts in the field, Applied Artificial Intelligence is the best practical guide for business leaders looking to get true value from the adoption of machine learning technology.
If you have questions such as...
- How do I distinguish true value from AI hype?
- What are the best business use cases for AI established so far?
- How do I identify the best business case for AI adoption and evaluate opportunities?
- Should I build or buy an AI platform?
- How do I find and recruit top AI talent for my enterprise?
- How will bringing AI into my business increase revenue or decrease costs?
- How can I facilitate AI adoption within my company?
... then this handbook provides you answers.
|Army of None: Autonomous Weapons and the Future of War
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Author: Paul Scharre
A Pentagon defense expert and former U.S. Army Ranger explores what it would mean to give machines authority over the ultimate decision of life or death.
What happens when a Predator drone has as much autonomy as a Google car? Or when a weapon that can hunt its own targets is hacked? Although it sounds like science fiction, the technology already exists to create weapons that can attack targets without human input. Paul Scharre, a leading expert in emerging weapons technologies, draws on deep research and firsthand experience to explore how these next-generation weapons are changing warfare.
Scharre’s far-ranging investigation examines the emergence of autonomous weapons, the movement to ban them, and the legal and ethical issues surrounding their use. He spotlights artificial intelligence in military technology, spanning decades of innovation from German noise-seeking Wren torpedoes in World War II―antecedents of today’s homing missiles―to autonomous cyber weapons, submarine-hunting robot ships, and robot tank armies. Through interviews with defense experts, ethicists, psychologists, and activists, Scharre surveys what challenges might face "centaur warfighters" on future battlefields, which will combine human and machine cognition. We’ve made tremendous technological progress in the past few decades, but we have also glimpsed the terrifying mishaps that can result from complex automated systems―such as when advanced F-22 fighter jets experienced a computer meltdown the first time they flew over the International Date Line.
At least thirty countries already have defensive autonomous weapons that operate under human supervision. Around the globe, militaries are racing to build robotic weapons with increasing autonomy. The ethical questions within this book grow more pressing each day. To what extent should such technologies be advanced? And if responsible democracies ban them, would that stop rogue regimes from taking advantage? At the forefront of a game-changing debate, Army of None engages military history, global policy, and cutting-edge science to argue that we must embrace technology where it can make war more precise and humane, but without surrendering human judgment. When the choice is life or death, there is no replacement for the human heart.
|Deep Learning with R
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Author: Francois Chollet
Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the Technology
Machine learning has made remarkable progress in recent years. Deep-learning systems now enable previously impossible smart applications, revolutionizing image recognition and natural-language processing, and identifying complex patterns in data. The Keras deep-learning library provides data scientists and developers working in R a state-of-the-art toolset for tackling deep-learning tasks.
About the Book
Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. Initially written for Python as Deep Learning with Python by Keras creator and Google AI researcher François Chollet and adapted for R by RStudio founder J. J. Allaire, this book builds your understanding of deep learning through intuitive explanations and practical examples. You'll practice your new skills with R-based applications in computer vision, natural-language processing, and generative models.
- Deep learning from first principles
- Setting up your own deep-learning environment
- Image classification and generation
- Deep learning for text and sequences
About the Reader
You'll need intermediate R programming skills. No previous experience with machine learning or deep learning is assumed.
About the Authors
François Chollet is a deep-learning researcher at Google and the author of the Keras library.
J.J. Allaire is the founder of RStudio and the author of the R interfaces to TensorFlow and Keras.
Table of Contents
PART 1 - FUNDAMENTALS OF DEEP LEARNING
- What is deep learning?
- Before we begin: the mathematical building blocks of neural networks
- Getting started with neural networks
- Fundamentals of machine learning
PART 2 - DEEP LEARNING IN PRACTICE
- Deep learning for computer vision
- Deep learning for text and sequences
- Advanced deep-learning best practices
- Generative deep learning
|How to Create a Mind: The Secret of Human Thought Revealed
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Author: Ray Kurzweil
Brand: Penguin Books
The bold futurist and bestselling author explores the limitless potential of reverse-engineering the human brain
Ray Kurzweil is arguably today’s most influential—and often controversial—futurist. In How to Create a Mind, Kurzweil presents a provocative exploration of the most important project in human-machine civilization—reverse engineering the brain to understand precisely how it works and using that knowledge to create even more intelligent machines.
Kurzweil discusses how the brain functions, how the mind emerges from the brain, and the implications of vastly increasing the powers of our intelligence in addressing the world’s problems. He thoughtfully examines emotional and moral intelligence and the origins of consciousness and envisions the radical possibilities of our merging with the intelligent technology we are creating.
Certain to be one of the most widely discussed and debated science books of the year, How to Create a Mind is sure to take its place alongside Kurzweil’s previous classics which include Fantastic Voyage: Live Long Enough to Live Forever and The Age of Spiritual Machines.
From the Hardcover edition.
|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 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.
|Make Your Own Neural Network: An In-depth Visual Introduction For Beginners
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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.
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