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
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: Jason Brownlee
Publisher: Machine Learning Mastery
ISBN:
Category : Computers
Languages : en
Pages : 266
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Book Description
Deep learning is the most interesting and powerful machine learning technique right now. Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow. Tap into their power in a few lines of code using Keras, the best-of-breed applied deep learning library. In this Ebook, learn exactly how to get started and apply deep learning to your own machine learning projects.
Author: Paolo Perrotta
Publisher: Pragmatic Bookshelf
ISBN: 1680507710
Category : Computers
Languages : en
Pages : 437
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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: Eric LeMarque
Publisher:
ISBN: 9780553807653
Category : Athletes with disabilities
Languages : en
Pages : 0
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Book Description
In this gripping first-person account, former Olympian Eric LeMarque recounts a harrowing tale of survival—of eight days in the frozen wilderness, of losing his legs to frostbite, and coming face-to-face with death. But Eric’s ordeal on the mountain was only part of his struggle for survival—as he reveals, with startling candor, an even more harrowing and inspiring tale of fame and addiction, healing and triumph. On February 6, 2004, Eric, a former professional hockey player and expert snowboarder, set off for the top of 12,000-foot Mammoth Mountain in California’s vast Sierra Nevada mountain range. Wearing only a long-sleeve shirt, a thin wool hat, ski pants, and a lightweight jacket—and with only four pieces of gum for food—he soon found himself chest-high in snow, veering off the snowboard trail, and plunging into the wilderness. By nightfall he knew he was in a fight for his life…Surviving eight days in subfreezing temperatures, he would earn the name “The Miracle Man” by stunned National Guard Black Hawk Chopper rescuers. But Eric’s against-all-odds survival was no surprise to those who knew him. A gifted hockey player in his teens, he was later drafted by the Boston Bruins and a 1994 Olympian. But when his playing days were over, Eric felt adrift. Everything changed when he first tasted the rush of hard drugs—the highly addictive crystal meth—which filled a void left by hockey and fame. By the time Eric reached the peak of Mammoth Mountain in 2004, he was already dueling demons that had seized his soul. A riveting adventure, a brutal confessional, here Eric tells his remarkable story—his climb to success, his long and painful fall, and his ordeal in the wilderness. In the end, a man whose life had been based on athleticism would lose both his legs, relearn to walk—even snowboard—with prosthetics, and finally confront the ultimate test of survival: what it takes to find your way out of darkness, and—after so many lies—to tell truth… and begin to live again.
Author: Alexey Grigorev
Publisher: Simon and Schuster
ISBN: 1638351058
Category : Computers
Languages : en
Pages : 470
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Book Description
Time to flex your machine learning muscles! Take on the carefully designed challenges of the Machine Learning Bookcamp and master essential ML techniques through practical application. Summary In Machine Learning Bookcamp you will: Collect and clean data for training models Use popular Python tools, including NumPy, Scikit-Learn, and TensorFlow Apply ML to complex datasets with images Deploy ML models to a production-ready environment The only way to learn is to practice! In Machine Learning Bookcamp, you’ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image analysis, each new project builds on what you’ve learned in previous chapters. You’ll build a portfolio of business-relevant machine learning projects that hiring managers will be excited to see. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Master key machine learning concepts as you build actual projects! Machine learning is what you need for analyzing customer behavior, predicting price trends, evaluating risk, and much more. To master ML, you need great examples, clear explanations, and lots of practice. This book delivers all three! About the book Machine Learning Bookcamp presents realistic, practical machine learning scenarios, along with crystal-clear coverage of key concepts. In it, you’ll complete engaging projects, such as creating a car price predictor using linear regression and deploying a churn prediction service. You’ll go beyond the algorithms and explore important techniques like deploying ML applications on serverless systems and serving models with Kubernetes and Kubeflow. Dig in, get your hands dirty, and have fun building your ML skills! What's inside Collect and clean data for training models Use popular Python tools, including NumPy, Scikit-Learn, and TensorFlow Deploy ML models to a production-ready environment About the reader Python programming skills assumed. No previous machine learning knowledge is required. About the author Alexey Grigorev is a principal data scientist at OLX Group. He runs DataTalks.Club, a community of people who love data. Table of Contents 1 Introduction to machine learning 2 Machine learning for regression 3 Machine learning for classification 4 Evaluation metrics for classification 5 Deploying machine learning models 6 Decision trees and ensemble learning 7 Neural networks and deep learning 8 Serverless deep learning 9 Serving models with Kubernetes and Kubeflow
Author: Amir Beck
Publisher: SIAM
ISBN: 1611974984
Category : Mathematics
Languages : en
Pages : 476
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Book Description
The primary goal of this book is to provide a self-contained, comprehensive study of the main ?rst-order methods that are frequently used in solving large-scale problems. First-order methods exploit information on values and gradients/subgradients (but not Hessians) of the functions composing the model under consideration. With the increase in the number of applications that can be modeled as large or even huge-scale optimization problems, there has been a revived interest in using simple methods that require low iteration cost as well as low memory storage. The author has gathered, reorganized, and synthesized (in a unified manner) many results that are currently scattered throughout the literature, many of which cannot be typically found in optimization books. First-Order Methods in Optimization offers comprehensive study of first-order methods with the theoretical foundations; provides plentiful examples and illustrations; emphasizes rates of convergence and complexity analysis of the main first-order methods used to solve large-scale problems; and covers both variables and functional decomposition methods.
Author: Jason Brownlee
Publisher: Machine Learning Mastery
ISBN:
Category : Computers
Languages : en
Pages : 564
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Book Description
Step-by-step tutorials on deep learning neural networks for computer vision in python with Keras.
Author: Jenny Odell
Publisher: Melville House
ISBN: 1612198554
Category : Technology & Engineering
Languages : en
Pages : 259
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Book Description
** A New York Times Bestseller ** NAMED ONE OF THE BEST BOOKS OF THE YEAR BY: Time • The New Yorker • NPR • GQ • Elle • Vulture • Fortune • Boing Boing • The Irish Times • The New York Public Library • The Brooklyn Public Library "A complex, smart and ambitious book that at first reads like a self-help manual, then blossoms into a wide-ranging political manifesto."—Jonah Engel Bromwich, The New York Times Book Review One of President Barack Obama's "Favorite Books of 2019" Porchlight's Personal Development & Human Behavior Book of the Year In a world where addictive technology is designed to buy and sell our attention, and our value is determined by our 24/7 data productivity, it can seem impossible to escape. But in this inspiring field guide to dropping out of the attention economy, artist and critic Jenny Odell shows us how we can still win back our lives. Odell sees our attention as the most precious—and overdrawn—resource we have. And we must actively and continuously choose how we use it. We might not spend it on things that capitalism has deemed important … but once we can start paying a new kind of attention, she writes, we can undertake bolder forms of political action, reimagine humankind’s role in the environment, and arrive at more meaningful understandings of happiness and progress. Far from the simple anti-technology screed, or the back-to-nature meditation we read so often, How to do Nothing is an action plan for thinking outside of capitalist narratives of efficiency and techno-determinism. Provocative, timely, and utterly persuasive, this book will change how you see your place in our world.
Author: Daphne Koller
Publisher: MIT Press
ISBN: 0262258358
Category : Computers
Languages : en
Pages : 1270
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Book Description
A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions. Most tasks require a person or an automated system to reason—to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.
Author: Kevin Kelly
Publisher: Basic Books
ISBN: 078674703X
Category : Science
Languages : en
Pages : 528
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Book Description
Out of Control chronicles the dawn of a new era in which the machines and systems that drive our economy are so complex and autonomous as to be indistinguishable from living things.
