Free To Learn

Our children spend their days being passively instructed, and made to sit still and take tests—often against their will. We call this imprisonment schooling, yet wonder why kids become bored and misbehave. Even outside of school children today seldom play and explore without adult supervision, and are afforded few opportunities to control their own lives. The result: anxious, unfocused children who see schooling—and life—as a series of hoops to struggle through. In Free to Learn, developmental psychologist Peter Gray argues that our children, if free to pursue their own interests through play, will not only learn all they need to know, but will do so with energy and passion. Children come into this world burning to learn, equipped with the curiosity, playfulness, and sociability to direct their own education. Yet we have squelched such instincts in a school model originally developed to indoctrinate, not to promote intellectual growth. 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, Gray 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. This capacity to learn through play evolved long ago, in hunter-gatherer bands where children acquired the skills of the culture through their own initiatives. And these instincts still operate remarkably well today, as studies at alternative, democratically administered schools show. When children are in charge of their own education, they learn better—and at lower cost than the traditional model of coercive schooling. 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 promote their happiness and learning.

A leading expert in childhood development makes the case for why self-directed learning -- "unschooling" -- is the best way to get kids to learn. 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.

Bringing Up Bébé meets Last Child in the Woods in this “fascinating exploration of the importance of the outdoors to childhood development” (Kirkus Reviews) from a Swedish-American mother who sets out to discover if the nature-centric parenting philosophy of her native Scandinavia holds the key to healthier, happier lives for her American children. Could the Scandinavian philosophy of “There’s no such thing as bad weather, only bad clothes” hold the key to happier, healthier lives for American children? When Swedish-born Linda Åkeson McGurk moved to Indiana, she quickly learned that the nature-centric parenting philosophies of her native Scandinavia were not the norm. In Sweden, children play outdoors year-round, regardless of the weather, and letting babies nap outside in freezing temperatures is common and recommended by physicians. Preschoolers spend their days climbing trees, catching frogs, and learning to compost, and environmental education is a key part of the public-school curriculum. In the US, McGurk found the playgrounds deserted, and preschoolers were getting drilled on academics with little time for free play in nature. And when a swimming outing at a nearby creek ended with a fine from a park officer, McGurk realized that the parenting philosophies of her native country and her adopted homeland were worlds apart. Struggling to decide what was best for her family, McGurk embarked on a six-month journey to Sweden with her two daughters to see how their lives would change in a place where spending time in nature is considered essential to a good childhood. Insightful and lively, There’s No Such Thing as Bad Weather is a fascinating personal narrative that illustrates how Scandinavian culture could hold the key to raising healthy, resilient, and confident children in America.

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.

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.

Education has become synonymous with schooling, but it doesn't have to be. As schooling becomes increasingly standardized and test driven, occupying more of childhood than ever before, parents and educators are questioning the role of schooling in society. Many are now exploring and creating alternatives. In a compelling narrative that introduces historical and contemporary research on self-directed education, Unschooled also spotlights how a diverse group of individuals and organizations are evolving an old schooling model of education. These innovators challenge the myth that children need to be taught in order to learn. They are parents who saw firsthand how schooling can dull children's natural curiosity and exuberance and others who decided early on to enable their children to learn without school. Educators who left public school classrooms discuss launching self-directed learning centers to allow young people's innate learning instincts to flourish, and entrepreneurs explore their disillusionment with the teach-and-test approach of traditional schooling.

Products, technologies, and workplaces change so quickly today that everyone is continually learning. Many of us are also teaching, even when it's not in our job descriptions. Whether it's giving a presentation, writing documentation, or creating a website or blog, we need and want to share our knowledge with other people. But if you've ever fallen asleep over a boring textbook, or fast-forwarded through a tedious e-learning exercise, you know that creating a great learning experience is harder than it seems. In Design For How People Learn, you'll discover how to use the key principles behind learning, memory, and attention to create materials that enable your audience to both gain and retain the knowledge and skills you're sharing. Using accessible visual metaphors and concrete methods and examples, Design For How People Learn will teach you how to leverage the fundamental concepts of instructional design both to improve your own learning and to engage your audience.

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