Deep Learning
Deep Learning

Author: Ian Goodfellow

Publisher: MIT Press

Published: 2016-11-10

Total Pages: 801

ISBN-13: 0262337371

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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.

Free Learning
Free Learning

Author: Ross Parker

Publisher: Routledge

Published: 2021-11-29

Total Pages: 369

ISBN-13: 100048369X

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This edited volume explores, investigates and analyses Free Learning – an innovative approach to student-directed learning which seeks to challenge educational norms from within. The volume is framed by a recognition of the urgent need for transformation of our educational systems. In traditional education, students work through a teacher-directed linear syllabus, at a pace dictated by the teacher, with summative assessment hurdles at too-frequent intervals. Progression and direction are determined solely by the teacher. In Free Learning, students determine their own learning pathway through a non-linear syllabus, which can be visualised as a mind map. Students may then complete as many units as they either have time for or are interested in, moving from one unit to another on the basis of having formatively satisfied the demands of each individual unit. This volume showcases the value and potential of Free Learning in contemporary practice and is intended to bridge theory and practice. The structure of the book reflects this complementary fit, with contributions from practitioners describing Free Learning as a learning and teaching tool in a range of educational settings, subjects and age-ranges. It also contains qualitative and quantitative analyses by researchers exploring the uptake of Free Learning and students’ responses to the methodology. Researchers and educators who are interested in student-directed methodologies, especially in Asia, will find that the practical accounts and analyses of Free Learning contained within provide much food for thought with regard to redefining student learning.

An Introduction to Statistical Learning
An Introduction to Statistical Learning

Author: Gareth James

Publisher: Springer Nature

Published: 2023-08-01

Total Pages: 617

ISBN-13: 3031387473

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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 for Coders with fastai and PyTorch
Deep Learning for Coders with fastai and PyTorch

Author: Jeremy Howard

Publisher: O'Reilly Media

Published: 2020-06-29

Total Pages: 624

ISBN-13: 1492045497

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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

Learning How to Learn
Learning How to Learn

Author: Barbara Oakley, PhD

Publisher: Penguin

Published: 2018-08-07

Total Pages: 258

ISBN-13: 052550446X

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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.

Free to Learn
Free to Learn

Author: Peter Gray

Publisher: Basic Books

Published: 2013-03-05

Total Pages: 223

ISBN-13: 0465037917

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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 to Bake Allergen-Free
Learning to Bake Allergen-Free

Author: Colette Martin

Publisher: The Experiment

Published: 2012-06-19

Total Pages: 305

ISBN-13: 1615190538

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Explains the most common food allergens and gluten, offers tips for adapting recipes, and provides recipes for meals, snacks, and desserts.

Create a Culture of Kindness in Middle School
Create a Culture of Kindness in Middle School

Author: Naomi Drew

Publisher: Free Spirit Publishing

Published: 2017-10-02

Total Pages: 273

ISBN-13: 1631981609

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Practical, research-based lessons for middle school educators to teach students pro-social attitudes and behaviors to prevent bullying. Create a Culture of Kindness in Middle School focuses on positive and pro-social attitudes and behaviors that build a respectful and compassionate school environment, while also addressing the tough issues of prejudice, anger, exclusion, and bullying. Through role-playing, perspective-taking, sharing, writing, discussion, and more, students develop the insights and skills they need to accept differences, resolve conflicts peacefully, stop bullying among peers, and create a community of kindness in their classrooms and school. Based on survey data gathered by the authors from more than 1,000 students, the book’s research-based lessons are easy to implement and developmentally appropriate. Digital content includes student handouts from the book.

Lessons Without Limit
Lessons Without Limit

Author: John Howard Falk

Publisher: Rowman Altamira

Published: 2002

Total Pages: 212

ISBN-13: 9780759101609

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Not just another book about school reform, 'Lessons Without Limit' is a guide to transforming the entire experience of learning across a lifetime.

Universal Design for Learning
Universal Design for Learning

Author: Anne Meyer

Publisher: CAST Professional Publishing

Published: 2015-12

Total Pages: 234

ISBN-13: 9781930583542

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Anne Meyer and David Rose, who first laid out the principles of UDL, provide an ambitious, engaging discussion of new research and best practices. This book gives the UDL field an essential and authoritative learning resource for the coming years. In the 1990s, Anne Meyer, David Rose, and their colleagues at CAST introduced Universal Design for Learning (UDL) as a framework to improve teaching and learning in the digital age, sparking an international reform movement. Now Meyer and Rose return with Universal Design for Learning: Theory and Practice, an up-to-date multimedia online book (with print and e-book options) that leverages more than a decade of research and implementation. This is the first significant new statement on UDL since 2002, an ambitious, engaging exploration of ideas and best practices that provides the growing UDL field with an essential and authoritative learning resource for the coming years. This new work includes contributions from CAST's research and implementation teams as well as from many of CAST's collaborators in schools, universities, and research settings. Readers are invited to contribute ideas, perspectives, and examples from their own practice in an online community of practice. --