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Author: Kieran O'Mahony
Publisher: Taylor & Francis
ISBN: 1040031285
Category : Education
Languages : en
Pages : 237
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Book Description
The Neural Teaching Guide showcases the innovative practices of K-12 teachers who are effectively applying findings from educational neuroscience into their classrooms. Educators today have remarkable opportunities to understand how the complex and often malleable functions of the brain affect learning, behavior, and social-emotional dynamics, but what practical strategies come out of this information? Authored by in-service teachers around the country, this book showcases a variety of brain-based approaches – cutting-edge yet intuitive, evidence-based yet accessibly translated – to helping children realize their potential at school. Both novice and veteran K-12 teachers alike will be reinvigorated to enhance students’ engagement and curiosity, nurture positive behaviors and self-regulation, support interest-based activities and inclusive interactions, identify biases and struggles, and more.
Author: Kieran O'Mahony
Publisher: Taylor & Francis
ISBN: 1040031285
Category : Education
Languages : en
Pages : 237
Get Book Here
Book Description
The Neural Teaching Guide showcases the innovative practices of K-12 teachers who are effectively applying findings from educational neuroscience into their classrooms. Educators today have remarkable opportunities to understand how the complex and often malleable functions of the brain affect learning, behavior, and social-emotional dynamics, but what practical strategies come out of this information? Authored by in-service teachers around the country, this book showcases a variety of brain-based approaches – cutting-edge yet intuitive, evidence-based yet accessibly translated – to helping children realize their potential at school. Both novice and veteran K-12 teachers alike will be reinvigorated to enhance students’ engagement and curiosity, nurture positive behaviors and self-regulation, support interest-based activities and inclusive interactions, identify biases and struggles, and more.
Author: Zaretta Hammond
Publisher: Corwin Press
ISBN: 1483308022
Category : Education
Languages : en
Pages : 290
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Book Description
A bold, brain-based teaching approach to culturally responsive instruction To close the achievement gap, diverse classrooms need a proven framework for optimizing student engagement. Culturally responsive instruction has shown promise, but many teachers have struggled with its implementation—until now. In this book, Zaretta Hammond draws on cutting-edge neuroscience research to offer an innovative approach for designing and implementing brain-compatible culturally responsive instruction. The book includes: Information on how one’s culture programs the brain to process data and affects learning relationships Ten “key moves” to build students’ learner operating systems and prepare them to become independent learners Prompts for action and valuable self-reflection
Author: João Arantes
Publisher:
ISBN: 9781734202526
Category :
Languages : en
Pages : 208
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Book Description
We are constantly learning, either we want or not. Learning is very natural to our brains, and it is (or it should be) enjoyable. What is the best way then to learn about effective learning? Making the process simple and fun. Cartoons are a very effective method because we learn with stories, we learn with observations and being part of a narrative. This is the idea of the authors, Harvard Professor and neuroscientist Felipe Fregni and Federal University of Sao Paulo Professor of Systems Thinking Joao Arantes, when they got together to write this cartoon book about learning and how to use this knowledge in teaching practices.This book follows the book of Critical Thinking in Teaching and Learning (Felipe Fregni, 2019) and in 191 cartoons the authors show using funny and simple illustrations the basic neural principles of learning (including the basic principles of neuroplasticity, how we encode new information, the attentional system and learning, memory and learning, the critical importance of our motivation system and how to activate that in educational programs, how stress affect (or help) learning and the use of social interaction in educational programs) and how to apply these principles for teaching (including teaching methods (student-centered vs. teacher-centered methods), online teaching, teaching critical thinking and assessments).We all need to become better learners, especially in a society when fact memorization is no longer important. This book is, therefore, an essential guide to every student and teacher looking to improving their own and their students learning experience.
Author: Mariale M. Hardiman
Publisher: Corwin Press
ISBN: 1412991986
Category : Education
Languages : en
Pages : 257
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Book Description
Compatible with other professional development programs, this model shows how to apply relevant research from educational and cognitive neuroscience to classroom settings through a pedagogical framework. The model's six components are: 1) Establish the emotional connection to learning; 2) Develop the physical learning environment; 3) Design the learning experience; 4) Teach for the mastery of content, skills, and concepts; 5) Teach for the extension and application of knowledge; 6) Evaluate learning. --Book cover.
Author: JoAnn Deak
Publisher:
ISBN: 9780982993804
Category : Juvenile Nonfiction
Languages : en
Pages : 0
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Book Description
Teaches children that they have the ability to stretch and grow their own brains, delivers the crucial message that mistakes are an essential part of learning, and introduces the brain's anatomy and functions.
Author: Charu C. Aggarwal
Publisher: Springer
ISBN: 3319944630
Category : Computers
Languages : en
Pages : 512
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Book Description
This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.
Author: Ahmed Fawzy Gad
Publisher: Academic Press
ISBN: 0323909345
Category : Medical
Languages : en
Pages : 302
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Book Description
Introduction to Deep Learning and Neural Networks with PythonTM: A Practical Guide is an intensive step-by-step guide for neuroscientists to fully understand, practice, and build neural networks. Providing math and PythonTM code examples to clarify neural network calculations, by book's end readers will fully understand how neural networks work starting from the simplest model Y=X and building from scratch. Details and explanations are provided on how a generic gradient descent algorithm works based on mathematical and PythonTM examples, teaching you how to use the gradient descent algorithm to manually perform all calculations in both the forward and backward passes of training a neural network. - Examines the practical side of deep learning and neural networks - Provides a problem-based approach to building artificial neural networks using real data - Describes PythonTM functions and features for neuroscientists - Uses a careful tutorial approach to describe implementation of neural networks in PythonTM - Features math and code examples (via companion website) with helpful instructions for easy implementation
Author: Melanie Mitchell
Publisher: Farrar, Straus and Giroux
ISBN: 0374715238
Category : Computers
Languages : en
Pages : 216
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Book Description
“After reading Mitchell’s guide, you’ll know what you don’t know and what other people don’t know, even though they claim to know it. And that’s invaluable." –The New York Times A leading computer scientist brings human sense to the AI bubble No recent scientific enterprise has proved as alluring, terrifying, and filled with extravagant promise and frustrating setbacks as artificial intelligence. The award-winning author Melanie Mitchell, a leading computer scientist, now reveals AI’s turbulent history and the recent spate of apparent successes, grand hopes, and emerging fears surrounding it. In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent—really—are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us? Along the way, she introduces the dominant models of modern AI and machine learning, describing cutting-edge AI programs, their human inventors, and the historical lines of thought underpinning recent achievements. She meets with fellow experts such as Douglas Hofstadter, the cognitive scientist and Pulitzer Prize–winning author of the modern classic Gödel, Escher, Bach, who explains why he is “terrified” about the future of AI. She explores the profound disconnect between the hype and the actual achievements in AI, providing a clear sense of what the field has accomplished and how much further it has to go. Interweaving stories about the science of AI and the people behind it, Artificial Intelligence brims with clear-sighted, captivating, and accessible accounts of the most interesting and provocative modern work in the field, flavored with Mitchell’s humor and personal observations. This frank, lively book is an indispensable guide to understanding today’s AI, its quest for “human-level” intelligence, and its impact on the future for us all.
Author: Mark A. Kramer
Publisher: MIT Press
ISBN: 0262529378
Category : Science
Languages : en
Pages : 385
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Book Description
A practical guide to neural data analysis techniques that presents sample datasets and hands-on methods for analyzing the data. As neural data becomes increasingly complex, neuroscientists now require skills in computer programming, statistics, and data analysis. This book teaches practical neural data analysis techniques by presenting example datasets and developing techniques and tools for analyzing them. Each chapter begins with a specific example of neural data, which motivates mathematical and statistical analysis methods that are then applied to the data. This practical, hands-on approach is unique among data analysis textbooks and guides, and equips the reader with the tools necessary for real-world neural data analysis. The book begins with an introduction to MATLAB, the most common programming platform in neuroscience, which is used in the book. (Readers familiar with MATLAB can skip this chapter and might decide to focus on data type or method type.) The book goes on to cover neural field data and spike train data, spectral analysis, generalized linear models, coherence, and cross-frequency coupling. Each chapter offers a stand-alone case study that can be used separately as part of a targeted investigation. The book includes some mathematical discussion but does not focus on mathematical or statistical theory, emphasizing the practical instead. References are included for readers who want to explore the theoretical more deeply. The data and accompanying MATLAB code are freely available on the authors' website. The book can be used for upper-level undergraduate or graduate courses or as a professional reference. A version of this textbook with all of the examples in Python is available on the MIT Press website.
Author: Michael R. Berthold
Publisher: Springer Nature
ISBN: 3030455742
Category : Computers
Languages : en
Pages : 427
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Book Description
Making use of data is not anymore a niche project but central to almost every project. With access to massive compute resources and vast amounts of data, it seems at least in principle possible to solve any problem. However, successful data science projects result from the intelligent application of: human intuition in combination with computational power; sound background knowledge with computer-aided modelling; and critical reflection of the obtained insights and results. Substantially updating the previous edition, then entitled Guide to Intelligent Data Analysis, this core textbook continues to provide a hands-on instructional approach to many data science techniques, and explains how these are used to solve real world problems. The work balances the practical aspects of applying and using data science techniques with the theoretical and algorithmic underpinnings from mathematics and statistics. Major updates on techniques and subject coverage (including deep learning) are included. Topics and features: guides the reader through the process of data science, following the interdependent steps of project understanding, data understanding, data blending and transformation, modeling, as well as deployment and monitoring; includes numerous examples using the open source KNIME Analytics Platform, together with an introductory appendix; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; integrates illustrations and case-study-style examples to support pedagogical exposition; supplies further tools and information at an associated website. This practical and systematic textbook/reference is a “need-to-have” tool for graduate and advanced undergraduate students and essential reading for all professionals who face data science problems. Moreover, it is a “need to use, need to keep” resource following one's exploration of the subject.
