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.

An essential resource for student and teacher clarity With the ever-changing landscape of education, teachers and leaders often find themselves searching for clarity in a sea of standards, curriculum resources, and competing priorities. Clarity for Learning offers a simple and doable approach to developing clarity and sharing it with students through five essential components: crafting learning intentions and success criteria co-constructing learning intentions and success criteria with learners creating opportunities for students to respond effective feedback on and for learning students and teachers sharing learning and progress The book is full of examples from teachers and leaders who have shared their journey, struggles, and successes for readers to use to propel their own work forward.

In this thought-provoking book, Frank Smith explains how schools and educational authorities systematically obstruct the powerful inherent learning abilities of children, creating handicaps that often persist through life. The author eloquently contrasts a false and fabricated “official theory” that learning is work (used to justify the external control of teachers and students through excessive regulation and massive testing) with a correct but officially suppressed “classic view” that learning is a social process that can occur naturally and continually through collaborative activities. This book will be crucial reading in a time when national authorities continue to blame teachers and students for alleged failures in education. It will help educators and parents to combat sterile attitudes toward teaching and learning and prevent current practices from doing further harm.

New Pedagogies for Deep Learning (NDPL) provides a comprehensive strategy for systemwide transformation. Using the 6 competencies of NDPL and a wealth of vivid examples, Fullan re-defines and re-examines what deep learning is and identifies the practical strategies for revolutionizing learning and leadership.

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.

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.

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. --

Like the first edition, the second edition of Learning by Doing: A Handbook for Professional Learning Communities at Work helps educators close the knowing-doing gap as they transform their schools into professional learning communities (PLCs).

This "provocative and personally searching"memoir follows one mother's story of enrolling her daughter in a local public school (San Francisco Chronicle), and the surprising, necessary lessons she learned with her neighbors. From the time Courtney E. Martin strapped her daughter, Maya, to her chest for long walks, she was curious about Emerson Elementary, a public school down the street from her Oakland home. She learned that White families in their gentrifying neighborhood largely avoided the majority-Black, poorly-rated school. As she began asking why, a journey of a thousand moral miles began. Learning in Public is the story, not just Courtney’s journey, but a whole country’s. Many of us are newly awakened to the continuing racial injustice all around us, but unsure of how to go beyond hashtags and yard signs to be a part of transforming the country. Courtney discovers that her public school, the foundation of our fragile democracy, is a powerful place to dig deeper. Courtney E. Martin examines her own fears, assumptions, and conversations with other moms and dads as they navigate school choice. A vivid portrait of integration’s virtues and complexities, and yes, the palpable joy of trying to live differently in a country re-making itself. Learning in Public might also set your family’s life on a different course forever.

Here is an informative introduction to language: its origins in the past, its growth through history, and its present use for communication between peoples. It is at the same time a history of language, a guide to foreign tongues, and a method for learning them. It shows, through basic vocabularies, family resemblances of languages -- Teutonic, Romance, Greek -- helpful tricks of translation, key combinations of roots and phonetic patterns. It presents by common-sense methods the most helpful approach to the mastery of many languages; it condenses vocabulary to a minimum of essential words; it simplifies grammar in an entirely new way; and it teaches a language as it is actually used in everyday life.