Free To Learn
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Free To Learn
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Author : Peter Gray
language : en
Publisher: Basic Books
Release Date : 2013-03-05
Free To Learn written by Peter Gray and has been published by Basic Books this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-05 with Education categories.
A leading expert in childhood development makes the case for why self-directed learning — "unschooling" — is the best way to get kids to learn. "All kids love learning. Most don't love school. That's a disconnect we've avoided discussing—until this lightning bolt of a book. If you've ever wondered why your curious kid is turning into a sullen slug at school, Peter Gray's Free to Learn has the answer. He also has the antidote." —Lenore Skenazy, author of Free-Range Kids In Free to Learn, developmental psychologist Peter Gray argues that in order to foster children who will thrive in today's constantly changing world, we must entrust them to steer their own learning and development. Drawing on evidence from anthropology, psychology, and history, he demonstrates that free play is the primary means by which children learn to control their lives, solve problems, get along with peers, and become emotionally resilient. A brave, counterintuitive proposal for freeing our children from the shackles of the curiosity-killing institution we call school, Free to Learn suggests that it's time to stop asking what's wrong with our children, and start asking what's wrong with the system. It shows how we can act—both as parents and as members of society—to improve children's lives and to promote their happiness and learning.
Learning How To Learn
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Author : Barbara Oakley, PhD
language : en
Publisher: Penguin
Release Date : 2018-08-07
Learning How To Learn written by Barbara Oakley, PhD and has been published by Penguin this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-07 with Juvenile Nonfiction categories.
A surprisingly simple way for students to master any subject--based on one of the world's most popular online courses and the bestselling book A Mind for Numbers A Mind for Numbers and its wildly popular online companion course "Learning How to Learn" have empowered more than two million learners of all ages from around the world to master subjects that they once struggled with. Fans often wish they'd discovered these learning strategies earlier and ask how they can help their kids master these skills as well. Now in this new book for kids and teens, the authors reveal how to make the most of time spent studying. We all have the tools to learn what might not seem to come naturally to us at first--the secret is to understand how the brain works so we can unlock its power. This book explains: Why sometimes letting your mind wander is an important part of the learning process How to avoid "rut think" in order to think outside the box Why having a poor memory can be a good thing The value of metaphors in developing understanding A simple, yet powerful, way to stop procrastinating Filled with illustrations, application questions, and exercises, this book makes learning easy and fun.
An Introduction To Statistical Learning
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Author : Gareth James
language : en
Publisher: Springer Nature
Release Date : 2023-06-30
An Introduction To Statistical Learning written by Gareth James and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-30 with Mathematics categories.
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, marketing, and 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, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
Deep Learning
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Author : Ian Goodfellow
language : en
Publisher: MIT Press
Release Date : 2016-11-10
Deep Learning written by Ian Goodfellow and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-10 with Computers categories.
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.
R For Data Science
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Author : Hadley Wickham
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-12-12
R For Data Science written by Hadley Wickham and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-12 with Computers categories.
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
Deep Learning For Coders With Fastai And Pytorch
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Author : Jeremy Howard
language : en
Publisher: O'Reilly Media
Release Date : 2020-06-29
Deep Learning For Coders With Fastai And Pytorch written by Jeremy Howard and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-29 with Computers categories.
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Free To Learn
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Author : Peter Gray
language : en
Publisher: Hachette UK
Release Date : 2013-03-05
Free To Learn written by Peter Gray and has been published by Hachette UK this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-05 with Education categories.
A leading expert in childhood development makes the case for why self-directed learning — "unschooling" — is the best way to get kids to learn. "All kids love learning. Most don't love school. That's a disconnect we've avoided discussing—until this lightning bolt of a book. If you've ever wondered why your curious kid is turning into a sullen slug at school, Peter Gray's Free to Learn has the answer. He also has the antidote." —Lenore Skenazy, author of Free-Range Kids In Free to Learn, developmental psychologist Peter Gray argues that in order to foster children who will thrive in today's constantly changing world, we must entrust them to steer their own learning and development. Drawing on evidence from anthropology, psychology, and history, he demonstrates that free play is the primary means by which children learn to control their lives, solve problems, get along with peers, and become emotionally resilient. A brave, counterintuitive proposal for freeing our children from the shackles of the curiosity-killing institution we call school, Free to Learn suggests that it's time to stop asking what's wrong with our children, and start asking what's wrong with the system. It shows how we can act—both as parents and as members of society—to improve children's lives and to promote their happiness and learning.
Free To Learn
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Author : Pam Laricchia
language : en
Publisher:
Release Date : 2012
Free To Learn written by Pam Laricchia and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Alternative education categories.
If you're eager to share the wonders of life in the real world with your children, to help them explore a world larger and more exciting than school's four walls and rigid programs can offer, let me share my unschooling experience with you. Thinking about homeschooling? Curious about unschooling? Walk with me as I share the five paradigm-changing ideas about learning and living that freed my family from the school schedule. With over ten years of experience, I have come to see how key these ideas were, and still are, to our unschooling lives. With stories, examples, and clear language, Free to Learn explores the depth and potential of unschooling. Learning freely, living joyfully.
Fluent Forever Revised Edition
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Author : Gabriel Wyner
language : en
Publisher: Random House
Release Date : 2024-12-31
Fluent Forever Revised Edition written by Gabriel Wyner and has been published by Random House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-31 with Foreign Language Study categories.
The bestselling guide to learning a new language and remembering what you learned, now revised and updated “A brilliant and thoroughly modern guide . . . If you want a new language to stick, start here.”—Gary Marcus, cognitive psychologist and author of the New York Times bestseller Guitar Zero Gabriel Wyner speaks seven foreign languages fluently. He didn’t learn them in school—who does? Rather, he mastered each one on his own, drawing on free online resources, short practice sessions, and his knowledge of neuroscience and linguistics. In Fluent Forever, Wyner shares his foolproof method for learning any language. It starts by hacking the way your brain naturally encodes information. You’ll discover how to hear new sounds and train your tongue to produce them accurately. You’ll connect spellings and sounds to images so that you start thinking in a new language without translating. With spaced-repetition systems, you’ll build a foundation for your language in a week and learn hundreds of words a month—with just a few minutes of practice each day. This revised edition also shares fresh strategies that Wyner has refined over years of study. You’ll learn to • use your interests to curate vocabulary that you’ll actually be excited to study • fast-track fluency, with a new appendix devoted to conversation strategies with native speakers • compile the best language-learning tool kit for your budget • harness the science of motivation and habit building to turbocharge your progress • find the perfect level of difficulty with reading and listening comprehension to stay engaged and avoid frustration With suggestions for helpful study aids and a wealth of free resources, the intuitive techniques in this book will offer you the most efficient and rewarding way to learn a new language.
Reinforcement Learning Second Edition
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Author : Richard S. Sutton
language : en
Publisher: MIT Press
Release Date : 2018-11-13
Reinforcement Learning Second Edition written by Richard S. Sutton and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-13 with Computers categories.
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.