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|Intelligent Tutoring Systems: 11th International Conference, ITS 2012, Chania, Crete, Greece, June 14-18, 2012. Proceedings (Lecture Notes in Computer Science)
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Brand: Brand: Springer
This book constitutes the refereed proceedings of the 11th International Conference on Intelligent Tutoring Systems, ITS 2012, held in Chania, Crete, Greece, in June 2012. The 28 revised full papers, 50 short papers, and 56 posters presented were carefully viewed and selected from 177 submissions. The specific theme of the ITS 2012 conference is co-adaption between technologies and human learning. Besides that, the highly interdisciplinary ITS conferences bring together researchers in computer science, informatics, and artificial intelligence on the one side - and cognitive science, educational psychology, and linguistics on the other side. The papers are organized in topical sections on affect/emotions, affect/signals, games/motivation and design, games/empirical studies, content representation, feedback, non conventional approaches, conceptual content representation, assessment constraints, dialogue, dialogue/questions, learner modeling, learning detection, interaction strategies for games, and empirical studies thereof in general.
- Used Book in Good Condition
|Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms
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Author: Nikhil Buduma
With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that’s paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field.
Companies such as Google, Microsoft, and Facebook are actively growing in-house deep-learning teams. For the rest of us, however, deep learning is still a pretty complex and difficult subject to grasp. If you’re familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started.
- Examine the foundations of machine learning and neural networks
- Learn how to train feed-forward neural networks
- Use TensorFlow to implement your first neural network
- Manage problems that arise as you begin to make networks deeper
- Build neural networks that analyze complex images
- Perform effective dimensionality reduction using autoencoders
- Dive deep into sequence analysis to examine language
- Understand the fundamentals of reinforcement learning
- Fundamentals of Deep Learning Designing Next Generation Machine Intelligence Algorithms
|I Am a Strange Loop
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Author: Douglas R. Hofstadter
Brand: Basic Books
I Am a Strange Loop
|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.
|Compressive Sensing Based Algorithms for Electronic Defence (Signals and Communication Technology)
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Author: Amit Kumar Mishra
This book details some of the major developments in the implementation of compressive sensing in radio applications for electronic defense and warfare communication use. It provides a comprehensive background to the subject and at the same time describes some novel algorithms. It also investigates application value and performance-related parameters of compressive sensing in scenarios such as direction finding, spectrum monitoring, detection, and classification.
|Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies
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Author: Steven Finlay
Artificial Intelligence (AI) and Machine Learning are now mainstream business tools. They are being applied across many industries to increase profits, reduce costs, save lives and improve customer experiences. Consequently, organisations which understand these tools and know how to use them are benefiting at the expense of their rivals. Artificial Intelligence and Machine Learning for Business cuts through the technical jargon that is often associated with these subjects. It delivers a simple and concise introduction for managers and business people. The focus is very much on practical application, and how to work with technical specialists (data scientists) to maximise the benefits of these technologies.
|Understanding Machine Learning: From Theory to Algorithms
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Author: Shai Shalev-Shwartz
Brand: Shai Shalev Shwartz Shai Ben David
Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.
- Understanding Machine Learning From Theory to Algorithms
- Foundations of Machine Learning (Adaptive Computation and Machine Learning series)
- Deep Learning (Adaptive Computation and Machine Learning series)
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
- Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
- Pattern Recognition and Machine Learning (Information Science and Statistics)
- Convex Optimization, With Corrections 2008
- Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning)
- Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (MIT Press)
- Computer Age Statistical Inference: Algorithms, Evidence, and Data Science (Institute of Mathematical Statistics Monographs)
- Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
|Humility Is the New Smart: Rethinking Human Excellence in the Smart Machine Age
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Author: Edward D. Hess
Brand: Hess Edward D
Humility Is the New Smart
Your job is at risk—if not now, then soon. We are on the leading edge of a Smart Machine Age led by artificial intelligence that will be as transformative for us as the Industrial Revolution was for our ancestors. Smart machines will take over millions of jobs in manufacturing, office work, the service sector, the professions, you name it. Not only can they know more data and analyze it faster than any mere human, say Edward Hess and Katherine Ludwig, but smart machines are free of the emotional, psychological, and cultural baggage that so often mars human thinking.
So we can’t beat ’em and we can’t join ’em. To stay relevant, we have to play a different game. Hess and Ludwig offer us that game plan. We need to excel at critical, creative, and innovative thinking and at genuinely engaging with others—things machines can’t do well. The key is to change our definition of what it means to be smart. Hess and Ludwig call it being NewSmart. In this extraordinarily timely book, they offer detailed guidance for developing NewSmart attitudes and four critical behaviors that will help us adapt to the new reality.
The crucial mindset underlying NewSmart is humility—not self-effacement but an accurate self-appraisal: acknowledging you can’t have all the answers, remaining open to new ideas, and committing yourself to lifelong learning. Drawing on extensive multidisciplinary research, Hess and Ludwig emphasize that the key to success in this new era is not to be more like the machines but to excel at the best of what makes us human.
- Humility Is the New Smart Rethinking Human Excellence in the Smart Machine Age
|Machine Learning For Dummies
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Author: John Paul Mueller
Your no-nonsense guide to making sense of machine learning
Machine learning can be a mind-boggling concept for the masses, but those who are in the trenches of computer programming know just how invaluable it is. Without machine learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation, and email spam filtering wouldn't be possible, and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks.
Covering the entry-level topics needed to get you familiar with the basic concepts of machine learning, this guide quickly helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality. Whether you're maddened by the math behind machine learning, apprehensive about AI, perplexed by preprocessing data—or anything in between—this guide makes it easier to understand and implement machine learning seamlessly.
- Grasp how day-to-day activities are powered by machine learning
- Learn to 'speak' certain languages, such as Python and R, to teach machines to perform pattern-oriented tasks and data analysis
- Learn to code in R using R Studio
- Find out how to code in Python using Anaconda
Dive into this complete beginner's guide so you are armed with all you need to know about machine learning!
|The Future of Leadership: Rise of Automation, Robotics and Artificial Intelligence
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Author: Brigette Tasha Hyacinth
Is Artificial Intelligence (AI) our greatest existential threat? Will AI take your Job? Is Privacy dead? Is Universal Basic Income a viable strategy or just a temporary bandage? Will AI solve all our problems? Will it make us happier? We can’t put the genie back in the bottle once it’s out. If we don't candidly answer the pertinent questions, we will only paint a false picture.
We are standing at a crucial and pivotal point in history. It’s time for diversity in AI. This unprecedented technology will affect society as a whole and we need individuals from diverse disciplines and backgrounds to join the discussion. The issues surrounding AI can’t be left to a small group of scientists, technologists or business executives to address. Our future and our children's future are at stake.
More than ever, we need leaders who will stand on integrity and who will put people first.
Do you want to take a glimpse into the future of leadership? The Future of Leadership: Rise of Automation, Robotics and Artificial Intelligence offers the most comprehensive view of what is taking place in the world of AI and emerging technologies, and gives valuable insights that will allow you to successfully navigate the tsunami of technology that is coming our way.
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