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|Deep Learning (Adaptive Computation and Machine Learning series)
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List price: $80.00
Author: Ian Goodfellow
Brand: Goodfellow Ian
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.
"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject."
-- Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.
The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.
Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
|Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
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Author: Aurélien Géron
Brand: O Reilly Media
Graphics in this book are printed in black and white.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.
By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.
- Explore the machine learning landscape, particularly neural nets
- Use scikit-learn to track an example machine-learning project end-to-end
- Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
- Use the TensorFlow library to build and train neural nets
- Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
- Learn techniques for training and scaling deep neural nets
- Apply practical code examples without acquiring excessive machine learning theory or algorithm details
|Life 3.0: Being Human in the Age of Artificial Intelligence
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Author: Max Tegmark
New York Times Best Seller
How will Artificial Intelligence affect crime, war, justice, jobs, society and our very sense of being human? The rise of AI has the potential to transform our future more than any other technology—and there’s nobody better qualified or situated to explore that future than Max Tegmark, an MIT professor who’s helped mainstream research on how to keep AI beneficial.
How can we grow our prosperity through automation without leaving people lacking income or purpose? What career advice should we give today’s kids? How can we make future AI systems more robust, so that they do what we want without crashing, malfunctioning or getting hacked? Should we fear an arms race in lethal autonomous weapons? Will machines eventually outsmart us at all tasks, replacing humans on the job market and perhaps altogether? Will AI help life flourish like never before or give us more power than we can handle?
What sort of future do you want? This book empowers you to join what may be the most important conversation of our time. It doesn’t shy away from the full range of viewpoints or from the most controversial issues—from superintelligence to meaning, consciousness and the ultimate physical limits on life in the cosmos.
- Our Mathematical Universe: My Quest for the Ultimate Nature of Reality
- Superintelligence: Paths, Dangers, Strategies
- Our Final Invention: Artificial Intelligence and the End of the Human Era
- The Sentient Machine: The Coming Age of Artificial Intelligence
- Machine, Platform, Crowd: Harnessing Our Digital Future
- Scale: The Universal Laws of Growth, Innovation, Sustainability, and the Pace of Life in Organisms, Cities, Economies, and Companies
- The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
- Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies
- The Industries of the Future
- How to Create a Mind: The Secret of Human Thought Revealed
|Superintelligence: Paths, Dangers, Strategies
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Author: Nick Bostrom
Brand: Bostrom Nick
A New York Times bestseller
Superintelligence asks the questions: What happens when machines surpass humans in general intelligence? Will artificial agents save or destroy us? Nick Bostrom lays the foundation for understanding the future of humanity and intelligent life.
The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant position. If machine brains surpassed human brains in general intelligence, then this new superintelligence could become extremely powerful - possibly beyond our control. As the fate of the gorillas now depends more on humans than on the species itself, so would the fate of humankind depend on the actions of the machine superintelligence.
But we have one advantage: we get to make the first move. Will it be possible to construct a seed Artificial Intelligence, to engineer initial conditions so as to make an intelligence explosion survivable? How could one achieve a controlled detonation?
This profoundly ambitious and original book breaks down a vast track of difficult intellectual terrain. After an utterly engrossing journey that takes us to the frontiers of thinking about the human condition and the future of intelligent life, we find in Nick Bostrom's work nothing less than a reconceptualization of the essential task of our time.
- Superintelligence Paths Dangers Strategies
|Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition
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Author: Sebastian Raschka
- Second edition of the bestselling book on Machine Learning
- A practical approach to key frameworks in data science, machine learning, and deep learning
- Use the most powerful Python libraries to implement machine learning and deep learning
- Get to know the best practices to improve and optimize your machine learning systems and algorithms
Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.
Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library.
Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you'll be ready to meet the new data analysis opportunities in today's world.
If you've read the first edition of this book, you'll be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You'll be able to learn and work with TensorFlow more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn.
What you will learn
- Understand the key frameworks in data science, machine learning, and deep learning
- Harness the power of the latest Python open source libraries in machine learning
- Explore machine learning techniques using challenging real-world data
- Master deep neural network implementation using the TensorFlow library
- Learn the mechanics of classification algorithms to implement the best tool for the job
- Predict continuous target outcomes using regression analysis
- Uncover hidden patterns and structures in data with clustering
- Delve deeper into textual and social media data using sentiment analysis
Table of Contents
- Giving Computers the Ability to Learn from Data
- Training Simple Machine Learning Algorithms for Classification
- A Tour of Machine Learning Classifiers Using Scikit-Learn
- Building Good Training Sets - Data Preprocessing
- Compressing Data via Dimensionality Reduction
- Learning Best Practices for Model Evaluation and Hyperparameter Tuning
- Combining Different Models for Ensemble Learning
- Applying Machine Learning to Sentiment Analysis
- Embedding a Machine Learning Model into a Web Application
- Predicting Continuous Target Variables with Regression Analysis
- Working with Unlabeled Data - Clustering Analysis
- Implementing a Multilayer Artificial Neural Network from Scratch
- Parallelizing Neural Network Training with TensorFlow
- Going Deeper - The Mechanics of TensorFlow
- Classifying Images with Deep Convolutional Neural Networks
- Modeling Sequential Data using Recurrent Neural Networks
|Gödel, Escher, Bach: An Eternal Golden Braid
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Author: Douglas R. Hofstadter
Brand: Douglas R Hofstadter
Winner of the Pulitzer Prize
A metaphorical fugue on minds and machines in the spirit of Lewis Carroll
Douglas Hofstadter's book is concerned directly with the nature of "maps" or links between formal systems. However, according to Hofstadter, the formal system that underlies all mental activity transcends the system that supports it. If life can grow out of the formal chemical substrate of the cell, if consciousness can emerge out of a formal system of firing neurons, then so too will computers attain human intelligence. Gödel, Escher, Bach is a wonderful exploration of fascinating ideas at the heart of cognitive science: meaning, reduction, recursion, and much more.
Twenty years after it topped the bestseller charts, Douglas R. Hofstadter's Gödel, Escher, Bach: An Eternal Golden Braid is still something of a marvel. Besides being a profound and entertaining meditation on human thought and creativity, this book looks at the surprising points of contact between the music of Bach, the artwork of Escher, and the mathematics of Gödel. It also looks at the prospects for computers and artificial intelligence (AI) for mimicking human thought. For the general reader and the computer techie alike, this book still sets a standard for thinking about the future of computers and their relation to the way we think.
Hofstadter's great achievement in Gödel, Escher, Bach was making abstruse mathematical topics (like undecidability, recursion, and 'strange loops') accessible and remarkably entertaining. Borrowing a page from Lewis Carroll (who might well have been a fan of this book), each chapter presents dialogue between the Tortoise and Achilles, as well as other characters who dramatize concepts discussed later in more detail. Allusions to Bach's music (centering on his Musical Offering) and Escher's continually paradoxical artwork are plentiful here. This more approachable material lets the author delve into serious number theory (concentrating on the ramifications of Gödel's Theorem of Incompleteness) while stopping along the way to ponder the work of a host of other mathematicians, artists, and thinkers.
The world has moved on since 1979, of course. The book predicted that computers probably won't ever beat humans in chess, though Deep Blue beat Garry Kasparov in 1997. And the vinyl record, which serves for some of Hofstadter's best analogies, is now left to collectors. Sections on recursion and the graphs of certain functions from physics look tantalizing, like the fractals of recent chaos theory. And AI has moved on, of course, with mixed results. Yet Gödel, Escher, Bach remains a remarkable achievement. Its intellectual range and ability to let us visualize difficult mathematical concepts help make it one of this century's best for anyone who's interested in computers and their potential for real intelligence. --Richard Dragan
Topics Covered: J.S. Bach, M.C. Escher, Kurt Gödel: biographical information and work, artificial intelligence (AI) history and theories, strange loops and tangled hierarchies, formal and informal systems, number theory, form in mathematics, figure and ground, consistency, completeness, Euclidean and non-Euclidean geometry, recursive structures, theories of meaning, propositional calculus, typographical number theory, Zen and mathematics, levels of description and computers; theory of mind: neurons, minds and thoughts; undecidability; self-reference and self-representation; Turing test for machine intelligence.
- Godel Escher Bach An Eternal Golden Braid
|An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
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Author: Gareth James
Brand: Brand: Springer
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.
Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
- Used Book in Good Condition
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
- Applied Predictive Modeling
- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
- Deep Learning (Adaptive Computation and Machine Learning series)
- Pattern Recognition and Machine Learning (Information Science and Statistics)
- Machine Learning with R - Second Edition: Expert techniques for predictive modeling to solve all your data analysis problems
- Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
- Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition
- Data Science from Scratch: First Principles with Python
- Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
|The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
Lowest new price: $52.90
Lowest used price: $49.11
List price: $89.95
Author: Trevor Hastie
This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.
This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates.
- The Elements of Statistical Learning Data Mining Inference and Prediction Second Edition Springer Series in Statistics
|The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
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Author: Pedro Domingos
Brand: Domingos Pedro
"Wonderfully erudite, humorous, and easy to read." --KDNuggets
In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner-the Master Algorithm-and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.
- The Master Algorithm How the Quest for the Ultimate Learning Machine Will Remake Our World
|Code: The Hidden Language of Computer Hardware and Software
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Author: Charles Petzold
What do flashlights, the British invasion, black cats, and seesaws have to do with computers? In CODE, they show us the ingenious ways we manipulate language and invent new means of communicating with each other. And through CODE, we see how this ingenuity and our very human compulsion to communicate have driven the technological innovations of the past two centuries.
Using everyday objects and familiar language systems such as Braille and Morse code, author Charles Petzold weaves an illuminating narrative for anyone who’s ever wondered about the secret inner life of computers and other smart machines.
It’s a cleverly illustrated and eminently comprehensible story—and along the way, you’ll discover you’ve gained a real context for understanding today’s world of PCs, digital media, and the Internet. No matter what your level of technical savvy, CODE will charm you—and perhaps even awaken the technophile within.
Charles Petzold's latest book, Code: The Hidden Language of Computer Hardware and Software, crosses over into general-interest nonfiction from his usual programming genre. It's a carefully written, carefully researched gem that will appeal to anyone who wants to understand computer technology at its essence. Readers learn about number systems (decimal, octal, binary, and all that) through Petzold's patient (and frequently entertaining) prose and then discover the logical systems that are used to process them. There's loads of historical information too. From Louis Braille's development of his eponymous raised-dot code to Intel Corporation's release of its early microprocessors, Petzold presents stories of people trying to communicate with (and by means of) mechanical and electrical devices. It's a fascinating progression of technologies, and Petzold presents a clear statement of how they fit together.
The real value of Code is in its explanation of technologies that have been obscured for years behind fancy user interfaces and programming environments, which, in the name of rapid application development, insulate the programmer from the machine. In a section on machine language, Petzold dissects the instruction sets of the genre-defining Intel 8080 and Motorola 6800 processors. He walks the reader through the process of performing various operations with each chip, explaining which opcodes poke which values into which registers along the way. Petzold knows that the hidden language of computers exhibits real beauty. In Code, he helps readers appreciate it. --David Wall
Topics covered: Mechanical and electrical representations of words and numbers, number systems, logic gates, performing mathematical operations with logic gates, microprocessors, machine code, memory and programming languages.
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