Author: Mark J. Lattery
Publisher: IAP
ISBN: 1681236303
Category : Education
Languages : en
Pages : 304
Get Book Here
Book Description
Deep Learning in Introductory Physics: Exploratory Studies of Model?Based Reasoning is concerned with the broad question of how students learn physics in a model?centered classroom. The diverse, creative, and sometimes unexpected ways students construct models, and deal with intellectual conflict, provide valuable insights into student learning and cast a new vision for physics teaching. This book is the first publication in several years to thoroughly address the “coherence versus fragmentation” debate in science education, and the first to advance and explore the hypothesis that deep science learning is regressive and revolutionary. Deep Learning in Introductory Physics also contributes to a growing literature on the use of history and philosophy of science to confront difficult theoretical and practical issues in science teaching, and addresses current international concern over the state of science education and appropriate standards for science teaching and learning. The book is divided into three parts. Part I introduces the framework, agenda, and educational context of the book. An initial study of student modeling raises a number of questions about the nature and goals of physics education. Part II presents the results of four exploratory case studies. These studies reproduce the results of Part I with a more diverse sample of students; under new conditions (a public debate, peer discussions, and group interviews); and with new research prompts (model?building software, bridging tasks, and elicitation strategies). Part III significantly advances the emergent themes of Parts I and II through historical analysis and a review of physics education research. ENDORSEMENTS: "In Deep Learning in Introductory Physics, Lattery describes his extremely innovative course in which students' ideas about motion are elicited, evaluated with peers, and revised through experiment and discussion. The reader can see the students' deep engagement in constructive scientific modeling, while students deal with counter-intuitive ideas about motion that challenged Galileo in many of the same ways. Lattery captures students engaging in scientific thinking skills, and building difficult conceptual understandings at the same time. This is the 'double outcome' that many science educators have been searching for. The case studies provide inspiring examples of innovative course design, student sensemaking and reasoning, and deep conceptual change." ~ John Clement, University of Massachusetts—Amherst, Scientific Reasoning Research Institute "Deep Learning in Introductory Physics is an extraordinary book and an important intellectual achievement in many senses. It offers new perspectives on science education that will be of interest to practitioners, to education researchers, as well as to philosophers and historians of science. Lattery combines insights into model-based thinking with instructive examples from the history of science, such as Galileo’s struggles with understanding accelerated motion, to introduce new ways of teaching science. The book is based on first-hand experiences with innovative teaching methods, reporting student’s ideas and discussions about motion as an illustration of how modeling and model-building can help understanding science. Its lively descriptions of these experiences and its concise presentations of insights backed by a rich literature on education, cognitive science, and the history and philosophy of science make it a great read for everybody interested in how models shape thinking processes." ~ Dr. Jürgen Renn, Director, Max Planck Institute for the History of Science
Author: Mark J. Lattery
Publisher: Information Age Publishing
ISBN: 9781681236292
Category : Physics
Languages : en
Pages : 0
Get Book Here
Book Description
A volume in Science & Engineering Education Sources Deep Learning in Introductory Physics: Exploratory Studies of Model‐Based Reasoning is concerned with the broad question of how students learn physics in a model‐centered classroom. The diverse, creative, and sometimes unexpected ways students construct models, and deal with intellectual conflict, provide valuable insights into student learning and cast a new vision for physics teaching. This book is the first publication in several years to thoroughly address the "coherence versus fragmentation" debate in science education, and the first to advance and explore the hypothesis that deep science learning is regressive and revolutionary. Deep Learning in Introductory Physics also contributes to a growing literature on the use of history and philosophy of science to confront difficult theoretical and practical issues in science teaching, and addresses current international concern over the state of science education and appropriate standards for science teaching and learning. The book is divided into three parts. Part I introduces the framework, agenda, and educational context of the book. An initial study of student modeling raises a number of questions about the nature and goals of physics education. Part II presents the results of four exploratory case studies. These studies reproduce the results of Part I with a more‐diverse sample of students; under new conditions (a public debate, peer discussions, and group interviews); and with new research prompts (model‐building software, bridging tasks, and elicitation strategies). Part III significantly advances the emergent themes of Parts I and II through historical analysis and a review of physics education research.
Author: Ijaz A. Rauf
Publisher: CRC Press
ISBN: 1000450473
Category : Computers
Languages : en
Pages : 176
Get Book Here
Book Description
Physics of Data Science and Machine Learning links fundamental concepts of physics to data science, machine learning, and artificial intelligence for physicists looking to integrate these techniques into their work. This book is written explicitly for physicists, marrying quantum and statistical mechanics with modern data mining, data science, and machine learning. It also explains how to integrate these techniques into the design of experiments, while exploring neural networks and machine learning, building on fundamental concepts of statistical and quantum mechanics. This book is a self-learning tool for physicists looking to learn how to utilize data science and machine learning in their research. It will also be of interest to computer scientists and applied mathematicians, alongside graduate students looking to understand the basic concepts and foundations of data science, machine learning, and artificial intelligence. Although specifically written for physicists, it will also help provide non-physicists with an opportunity to understand the fundamental concepts from a physics perspective to aid in the development of new and innovative machine learning and artificial intelligence tools. Key Features: Introduces the design of experiments and digital twin concepts in simple lay terms for physicists to understand, adopt, and adapt. Free from endless derivations; instead, equations are presented and it is explained strategically why it is imperative to use them and how they will help in the task at hand. Illustrations and simple explanations help readers visualize and absorb the difficult-to-understand concepts. Ijaz A. Rauf is an adjunct professor at the School of Graduate Studies, York University, Toronto, Canada. He is also an associate researcher at Ryerson University, Toronto, Canada and president of the Eminent-Tech Corporation, Bradford, ON, Canada.
Author: Stefan Kollmannsberger
Publisher: Springer Nature
ISBN: 3030765873
Category : Technology & Engineering
Languages : en
Pages : 108
Get Book Here
Book Description
This book provides a first course on deep learning in computational mechanics. The book starts with a short introduction to machine learning’s fundamental concepts before neural networks are explained thoroughly. It then provides an overview of current topics in physics and engineering, setting the stage for the book’s main topics: physics-informed neural networks and the deep energy method. The idea of the book is to provide the basic concepts in a mathematically sound manner and yet to stay as simple as possible. To achieve this goal, mostly one-dimensional examples are investigated, such as approximating functions by neural networks or the simulation of the temperature’s evolution in a one-dimensional bar. Each chapter contains examples and exercises which are either solved analytically or in PyTorch, an open-source machine learning framework for python.
Author: Steven L. Brunton
Publisher: Cambridge University Press
ISBN: 1009098489
Category : Computers
Languages : en
Pages : 615
Get Book Here
Book Description
A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Author: Mehryar Mohri
Publisher: MIT Press
ISBN: 0262351366
Category : Computers
Languages : en
Pages : 505
Get Book Here
Book Description
A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.
Author: Jose Mestre
Publisher: World Scientific
ISBN: 9811226563
Category : Science
Languages : en
Pages : 211
Get Book Here
Book Description
This book on the teaching and learning of physics is intended for college-level instructors, but high school instructors might also find it very useful.Some ideas found in this book might be a small 'tweak' to existing practices whereas others require more substantial revisions to instruction. The discussions of student learning herein are based on research evidence accumulated over decades from various fields, including cognitive psychology, educational psychology, the learning sciences, and discipline-based education research including physics education research. Likewise, the teaching suggestions are also based on research findings. As for any other scientific endeavor, physics education research is an empirical field where experiments are performed, data are analyzed and conclusions drawn. Evidence from such research is then used to inform physics teaching and learning.While the focus here is on introductory physics taken by most students when they are enrolled, however, the ideas can also be used to improve teaching and learning in both upper-division undergraduate physics courses, as well as graduate-level courses. Whether you are new to teaching physics or a seasoned veteran, various ideas and strategies presented in the book will be suitable for active consideration.
Author: John D. Kelleher
Publisher: MIT Press
ISBN: 0262537559
Category : Computers
Languages : en
Pages : 298
Get Book Here
Book Description
An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones, machine translation, AI games, driverless cars, and other applications. When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution. Kelleher explains that deep learning enables data-driven decisions by identifying and extracting patterns from large datasets; its ability to learn from complex data makes deep learning ideally suited to take advantage of the rapid growth in big data and computational power. Kelleher also explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art. He describes the most important deep learning architectures, including autoencoders, recurrent neural networks, and long short-term networks, as well as such recent developments as Generative Adversarial Networks and capsule networks. He also provides a comprehensive (and comprehensible) introduction to the two fundamental algorithms in deep learning: gradient descent and backpropagation. Finally, Kelleher considers the future of deep learning—major trends, possible developments, and significant challenges.
Author: Issam El Naqa
Publisher: Springer
ISBN: 3319183052
Category : Medical
Languages : en
Pages : 336
Get Book Here
Book Description
This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.
Author: Ronald T. Kneusel
Publisher: No Starch Press
ISBN: 1718500742
Category : Computers
Languages : en
Pages : 463
Get Book Here
Book Description
Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects. If you’ve been curious about artificial intelligence and machine learning but didn’t know where to start, this is the book you’ve been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning teaches you the why of deep learning and will inspire you to explore further. All you need is basic familiarity with computer programming and high school math—the book will cover the rest. After an introduction to Python, you’ll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models’ performance. You’ll also learn: How to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector Machines How neural networks work and how they’re trained How to use convolutional neural networks How to develop a successful deep learning model from scratch You’ll conduct experiments along the way, building to a final case study that incorporates everything you’ve learned. The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning will give you the skills and confidence to dive into your own machine learning projects.
