Eric Is Thirsty: Machine Learning for Kids: Gradient Descent PDF Download
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Author: Rocket Baby Club
Publisher: Rocket Baby Club
ISBN: 9781645164302
Category : Juvenile Nonfiction
Languages : en
Pages : 36
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Book Description
Eric the ladybug is an artist and traveler. He went to a mountain to watch the sunset and drew a painting of it. The next day when he woke up, he feels so thirsty and needs to find some water to drink. Will he be able to find the lowest point near him in order to find a water source? After an adventure with Eric the thirsty ladybug, you will know the most important intuition in machine learning, gradient descent.
Author: Rocket Baby Club
Publisher: Rocket Baby Club
ISBN: 9781645164302
Category : Juvenile Nonfiction
Languages : en
Pages : 36
Get Book Here
Book Description
Eric the ladybug is an artist and traveler. He went to a mountain to watch the sunset and drew a painting of it. The next day when he woke up, he feels so thirsty and needs to find some water to drink. Will he be able to find the lowest point near him in order to find a water source? After an adventure with Eric the thirsty ladybug, you will know the most important intuition in machine learning, gradient descent.
Author: Sean Gerrish
Publisher: MIT Press
ISBN: 0262537974
Category : Computers
Languages : en
Pages : 313
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Book Description
Everything you want to know about the breakthroughs in AI technology, machine learning, and deep learning—as seen in self-driving cars, Netflix recommendations, and more. The future is here: Self-driving cars are on the streets, an algorithm gives you movie and TV recommendations, IBM’s Watson triumphed on Jeopardy over puny human brains, computer programs can be trained to play Atari games. But how do all these things work? In this book, Sean Gerrish offers an engaging and accessible overview of the breakthroughs in artificial intelligence and machine learning that have made today’s machines so smart. Gerrish outlines some of the key ideas that enable intelligent machines to perceive and interact with the world. He describes the software architecture that allows self-driving cars to stay on the road and to navigate crowded urban environments; the million-dollar Netflix competition for a better recommendation engine (which had an unexpected ending); and how programmers trained computers to perform certain behaviors by offering them treats, as if they were training a dog. He explains how artificial neural networks enable computers to perceive the world—and to play Atari video games better than humans. He explains Watson’s famous victory on Jeopardy, and he looks at how computers play games, describing AlphaGo and Deep Blue, which beat reigning world champions at the strategy games of Go and chess. Computers have not yet mastered everything, however; Gerrish outlines the difficulties in creating intelligent agents that can successfully play video games like StarCraft that have evaded solution—at least for now. Gerrish weaves the stories behind these breakthroughs into the narrative, introducing readers to many of the researchers involved, and keeping technical details to a minimum. Science and technology buffs will find this book an essential guide to a future in which machines can outsmart people.
Author: Paolo Perrotta
Publisher: Pragmatic Bookshelf
ISBN: 1680507710
Category : Computers
Languages : en
Pages : 455
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Book Description
You've decided to tackle machine learning - because you're job hunting, embarking on a new project, or just think self-driving cars are cool. But where to start? It's easy to be intimidated, even as a software developer. The good news is that it doesn't have to be that hard. Master machine learning by writing code one line at a time, from simple learning programs all the way to a true deep learning system. Tackle the hard topics by breaking them down so they're easier to understand, and build your confidence by getting your hands dirty. Peel away the obscurities of machine learning, starting from scratch and going all the way to deep learning. Machine learning can be intimidating, with its reliance on math and algorithms that most programmers don't encounter in their regular work. Take a hands-on approach, writing the Python code yourself, without any libraries to obscure what's really going on. Iterate on your design, and add layers of complexity as you go. Build an image recognition application from scratch with supervised learning. Predict the future with linear regression. Dive into gradient descent, a fundamental algorithm that drives most of machine learning. Create perceptrons to classify data. Build neural networks to tackle more complex and sophisticated data sets. Train and refine those networks with backpropagation and batching. Layer the neural networks, eliminate overfitting, and add convolution to transform your neural network into a true deep learning system. Start from the beginning and code your way to machine learning mastery. What You Need: The examples in this book are written in Python, but don't worry if you don't know this language: you'll pick up all the Python you need very quickly. Apart from that, you'll only need your computer, and your code-adept brain.
Author: Brian Christian
Publisher: W. W. Norton & Company
ISBN: 039363583X
Category : Science
Languages : en
Pages : 458
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Book Description
"If you’re going to read one book on artificial intelligence, this is the one." —Stephen Marche, New York Times A jaw-dropping exploration of everything that goes wrong when we build AI systems and the movement to fix them. Today’s “machine-learning” systems, trained by data, are so effective that we’ve invited them to see and hear for us—and to make decisions on our behalf. But alarm bells are ringing. Recent years have seen an eruption of concern as the field of machine learning advances. When the systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge. Researchers call this the alignment problem. Systems cull résumés until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole—and appear to assess Black and White defendants differently. We can no longer assume that our mortgage application, or even our medical tests, will be seen by human eyes. And as autonomous vehicles share our streets, we are increasingly putting our lives in their hands. The mathematical and computational models driving these changes range in complexity from something that can fit on a spreadsheet to a complex system that might credibly be called “artificial intelligence.” They are steadily replacing both human judgment and explicitly programmed software. In best-selling author Brian Christian’s riveting account, we meet the alignment problem’s “first-responders,” and learn their ambitious plan to solve it before our hands are completely off the wheel. In a masterful blend of history and on-the ground reporting, Christian traces the explosive growth in the field of machine learning and surveys its current, sprawling frontier. Readers encounter a discipline finding its legs amid exhilarating and sometimes terrifying progress. Whether they—and we—succeed or fail in solving the alignment problem will be a defining human story. The Alignment Problem offers an unflinching reckoning with humanity’s biases and blind spots, our own unstated assumptions and often contradictory goals. A dazzlingly interdisciplinary work, it takes a hard look not only at our technology but at our culture—and finds a story by turns harrowing and hopeful.
Author: Pablo Duboue
Publisher: Cambridge University Press
ISBN: 1108709389
Category : Computers
Languages : en
Pages : 287
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Book Description
A practical guide for data scientists who want to improve the performance of any machine learning solution with feature engineering.
Author: Nancy W. Gleason
Publisher: Springer
ISBN: 9811301948
Category : Education
Languages : en
Pages : 238
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Book Description
This open access collection examines how higher education responds to the demands of the automation economy and the fourth industrial revolution. Considering significant trends in how people are learning, coupled with the ways in which different higher education institutions and education stakeholders are implementing adaptations, it looks at new programs and technological advances that are changing how and why we teach and learn. The book addresses trends in liberal arts integration of STEM innovations, the changing role of libraries in the digital age, global trends in youth mobility, and the development of lifelong learning programs. This is coupled with case study assessments of the various ways China, Singapore, South Africa and Costa Rica are preparing their populations for significant shifts in labour market demands – shifts that are already underway. Offering examples of new frameworks in which collaboration between government, industry, and higher education institutions can prevent lagging behind in this fast changing environment, this book is a key read for anyone wanting to understand how the world should respond to the radical technological shifts underway on the frontline of higher education.
Author: Sandro Skansi
Publisher: Springer
ISBN: 3319730045
Category : Computers
Languages : en
Pages : 196
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Book Description
This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website. Topics and features: introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks, and the recurrent connections to a feed-forward neural network; describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning; presents a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism. This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.
Author: Oliver Sacks
Publisher: Vintage
ISBN: 0345805887
Category : Psychology
Languages : en
Pages : 465
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Book Description
From the bestselling author of The Man Who Mistook His Wife for a Hat • Fascinating portraits of neurological disorder in which men, women, and one extraordinary child emerge as brilliantly adaptive personalities, whose conditions have not so much debilitated them as ushered them into another reality. Here are seven detailed narratives of neurological patients, including a surgeon consumed by the compulsive tics of Tourette's syndrome unless he is operating; an artist who loses all sense of color in a car accident, but finds a new sensibility and creative power in black and white; and an autistic professor who cannot decipher the simplest social exchange between humans, but has built a career out of her intuitive understanding of animal behavior. Sacks combines the well honed mind of an academician with the verve of a true storyteller.
Author: Steven S. Skiena
Publisher: Springer
ISBN: 3319554441
Category : Computers
Languages : en
Pages : 456
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Book Description
This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)
Author: Andrew Glassner
Publisher: No Starch Press
ISBN: 1718500734
Category : Computers
Languages : en
Pages : 1315
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Book Description
A richly-illustrated, full-color introduction to deep learning that offers visual and conceptual explanations instead of equations. You'll learn how to use key deep learning algorithms without the need for complex math. Ever since computers began beating us at chess, they've been getting better at a wide range of human activities, from writing songs and generating news articles to helping doctors provide healthcare. Deep learning is the source of many of these breakthroughs, and its remarkable ability to find patterns hiding in data has made it the fastest growing field in artificial intelligence (AI). Digital assistants on our phones use deep learning to understand and respond intelligently to voice commands; automotive systems use it to safely navigate road hazards; online platforms use it to deliver personalized suggestions for movies and books - the possibilities are endless. Deep Learning: A Visual Approach is for anyone who wants to understand this fascinating field in depth, but without any of the advanced math and programming usually required to grasp its internals. If you want to know how these tools work, and use them yourself, the answers are all within these pages. And, if you're ready to write your own programs, there are also plenty of supplemental Python notebooks in the accompanying Github repository to get you going. The book's conversational style, extensive color illustrations, illuminating analogies, and real-world examples expertly explain the key concepts in deep learning, including: • How text generators create novel stories and articles • How deep learning systems learn to play and win at human games • How image classification systems identify objects or people in a photo • How to think about probabilities in a way that's useful to everyday life • How to use the machine learning techniques that form the core of modern AI Intellectual adventurers of all kinds can use the powerful ideas covered in Deep Learning: A Visual Approach to build intelligent systems that help us better understand the world and everyone who lives in it. It's the future of AI, and this book allows you to fully envision it. Full Color Illustrations
