"I felt like I walked the walk and could implement many of the strategies after reading it once." —Andrea Golarz-Ziemba, Teacher O.P. Morton Elementary School, Hobart, IN "If our newest teachers came out of college with this knowledge, supplemented with the learning standards, their students would be more successful from day one." —Patti Larche, Director of Curriculum and Instruction Phelps-Clifton Springs CSD, NY Practical strategies to take your students′ understanding from "So what?" to "Wow!" With the amount of content that teachers have to teach, how can we ensure that students gain a deep and lasting understanding of what they have learned? Based on the most current research on cognition and the brain, this exciting book for Grades 4–12 provides teachers with a systematic, reflective approach to incorporating powerful learning and content processing techniques into everyday instruction. Nationally recognized experts Eric Jensen and LeAnn Nickelsen outline the Deeper Learning Cycle (DELC), an instructional model that incorporates brain research, standards, and individual learning differences, to help educators teach for deeper understanding and critical thinking. This invaluable resource includes: In-depth chapters explaining the seven stages of the DELC: planning with standards and curriculum, pre-assessing, building a positive learning culture, priming and activating prior knowledge, acquiring new knowledge, processing the learning, and evaluating student learning A Deeper Learning lesson plan template to apply to daily instruction 50 strategies to differentiate instruction based on learning levels and to help students process content in meaningful ways Reflection questions to help teachers apply the material to their own practice Deeper Learning is a powerful tool to promote and support student progress beyond the surface level of understanding in any subject area.

Education and educational systems represent a powerful source of success (or failure) for any political community. The emergence of AI and new technologies will progressively transform the labour market, leading to technological unemployment. In this scenario, promoting pedagogical strategies fostering deeper learning represents an ethical and pragmatic commitment to synchronize education with the ongoing socio-economic transformations. In line with the road ahead, the scope of the volume is to collect ideas, reflections and experiences related to deeper learning and skill development in the education environment.

Winner of the Grawemeyer Award “In their brave search for depth in American high schools, scholars Jal Mehta and Sarah Fine suffered many disappointments...Undeterred, they spent 750 hours observing classes, interviewed more than 300 people, and produced the best book on high school dynamics I have ever read.” —Jay Mathews, Washington Post “A hopeful, easy-to-read narrative on what the best teachers do and what deep, engaging learning looks like for students. Grab this text if you’re looking for a celebration of what’s possible in American schools.” —Edutopia “This is the first and only book to depict not just the constraints on good teaching, but also how good teachers transcend them. A superb book in every way: timely, lively, and entertaining.” —Jonathan Zimmerman, University of Pennsylvania What would it take to transform our high schools into places capable of supporting deep learning for students across a wide range of aptitudes and interests? To find out, Jal Mehta and Sarah Fine spent hundreds of hours observing and talking to teachers and students in and out of the classroom at thirty of the country’s most innovative schools. To their dismay, they discovered that deeper learning is more often the exception than the rule. And yet they found pockets of powerful learning at almost every school, often in extracurriculars but also in a few mold-breaking academic courses. So what must schools do to achieve the integrations that support deep learning: rigor with joy, precision with play, mastery with identity and creativity? In Search of Deeper Learning takes a deep dive into the state of our schools and lays out an inspiring new vision for American education.

Deep Learning with Keras This book will introduce you to various supervised and unsupervised deep learning algorithms like the multilayer perceptron, linear regression and other more advanced deep convolutional and recurrent neural networks. You will also learn about image processing, handwritten recognition, object recognition and much more. Furthermore, you will get familiar with recurrent neural networks like LSTM and GAN as you explore processing sequence data like time series, text, and audio. The book will definitely be your best companion on this great deep learning journey with Keras introducing you to the basics you need to know in order to take next steps and learn more advanced deep neural networks. Here Is a Preview of What You’ll Learn Here… The difference between deep learning and machine learning Deep neural networks Convolutional neural networks Building deep learning models with Keras Multi-layer perceptron network models Activation functions Handwritten recognition using MNIST Solving multi-class classification problems Recurrent neural networks and sequence classification And much more... Get this book NOW and learn more about Deep Learning with Keras!

Offers ready-made, theory-driven deeper-learning materials across subjects to show how to put a pluriliteracies approach into practice.

Deep Learning: A Comprehensive Guide provides comprehensive coverage of Deep Learning (DL) and Machine Learning (ML) concepts. DL and ML are the most sought-after domains, requiring a deep understanding – and this book gives no less than that. This book enables the reader to build innovative and useful applications based on ML and DL. Starting with the basics of neural networks, and continuing through the architecture of various types of CNNs, RNNs, LSTM, and more till the end of the book, each and every topic is given the utmost care and shaped professionally and comprehensively. Key Features Includes the smooth transition from ML concepts to DL concepts Line-by-line explanations have been provided for all the coding-based examples Includes a lot of real-time examples and interview questions that will prepare the reader to take up a job in ML/DL right away Even a person with a non-computer-science background can benefit from this book by following the theory, examples, case studies, and code snippets Every chapter starts with the objective and ends with a set of quiz questions to test the reader’s understanding Includes references to the related YouTube videos that provide additional guidance AI is a domain for everyone. This book is targeted toward everyone irrespective of their field of specialization. Graduates and researchers in deep learning will find this book useful.

A concise and practical exploration of key topics and applications in data science In Deep Learning, from Big Data to Artificial Intelligence, expert researcher Dr. Stéphane Tufféry delivers an insightful discussion of the applications of deep learning and big data that focuses on practical instructions on various software tools and deep learning methods relying on three major libraries: MXNet, PyTorch, and Keras-TensorFlow. In the book, numerous, up-to-date examples are combined with key topics relevant to modern data scientists, including processing optimization, neural network applications, natural language processing, and image recognition. This is a thoroughly revised and updated edition of a book originally released in French, with new examples and methods included throughout. Classroom-tested and intuitively organized, Deep Learning, from Big Data to Artificial Intelligence offers complimentary access to a companion website that provides R and Python source code for the examples offered in the book. Readers will also find: A thorough introduction to practical deep learning techniques with explanations and examples for various programming libraries Comprehensive explorations of a variety of applications for deep learning, including image recognition and natural language processing Discussions of the theory of deep learning, neural networks, and artificial intelligence linked to concrete techniques and strategies commonly used to solve real-world problems Perfect for graduate students studying data science, big data, deep learning, and artificial intelligence, Deep Learning, from Big Data to Artificial Intelligence will also earn a place in the libraries of data science researchers and practicing data scientists.

What Is Deep Learning Deep learning belongs to a larger family of machine learning approaches that are founded on artificial neural networks and representation learning. This family of methods is known as deep learning. There are three different ways to learn: supervised, semi-supervised, and unsupervised. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Deep learning Chapter 2: Machine learning Chapter 3: Neural coding Chapter 4: Scale space Chapter 5: Compressed sensing Chapter 6: Reservoir computing Chapter 7: Echo state network Chapter 8: Stochastic parrot Chapter 9: Differentiable programming Chapter 10: Liquid state machine (II) Answering the public top questions about deep learning. (III) Real world examples for the usage of deep learning in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of deep learning' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of deep learning.

This book constitutes the thoroughly refereed proceedings of the 2nd International Conference on Social Sciences, ICONESS 2023, held in Purwokerto, Indonesia, in 22-23 July 2023. The 88 full papers presented were carefully reviewed and selected from 198 submissions. The papers reflect the conference sessions as follows: Education (Curriculum and Instruction, Education and Development, Educational Psychology, Social Science Education, and Elementary Education); Religion (Islamic Education, Islamic Civilization, and Shariah Economic), and Literation (Teaching English as a Second Language/TESL, Language and Communication, Literacy).

Take your machine learning skills to the next level by mastering Deep Learning concepts and algorithms using Python. About This Book Explore and create intelligent systems using cutting-edge deep learning techniques Implement deep learning algorithms and work with revolutionary libraries in Python Get real-world examples and easy-to-follow tutorials on Theano, TensorFlow, H2O and more Who This Book Is For This book is for Data Science practitioners as well as aspirants who have a basic foundational understanding of Machine Learning concepts and some programming experience with Python. A mathematical background with a conceptual understanding of calculus and statistics is also desired. What You Will Learn Get a practical deep dive into deep learning algorithms Explore deep learning further with Theano, Caffe, Keras, and TensorFlow Learn about two of the most powerful techniques at the core of many practical deep learning implementations: Auto-Encoders and Restricted Boltzmann Machines Dive into Deep Belief Nets and Deep Neural Networks Discover more deep learning algorithms with Dropout and Convolutional Neural Networks Get to know device strategies so you can use deep learning algorithms and libraries in the real world In Detail With an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used broadly across different industries. The book will give you all the practical information available on the subject, including the best practices, using real-world use cases. You will learn to recognize and extract information to increase predictive accuracy and optimize results. Starting with a quick recap of important machine learning concepts, the book will delve straight into deep learning principles using Sci-kit learn. Moving ahead, you will learn to use the latest open source libraries such as Theano, Keras, Google's TensorFlow, and H20. Use this guide to uncover the difficulties of pattern recognition, scaling data with greater accuracy and discussing deep learning algorithms and techniques. Whether you want to dive deeper into Deep Learning, or want to investigate how to get more out of this powerful technology, you'll find everything inside. Style and approach Python Machine Learning by example follows practical hands on approach. It walks you through the key elements of Python and its powerful machine learning libraries with the help of real world projects.