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Principles And Labs For Deep Learning

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Principles and Labs for Deep Learning

Principles and Labs for Deep Learning Book
Author : Shih-Chia Huang,Trung-Hieu Le
Publisher : Academic Press
Release : 2021-07-06
ISBN : 0323901999
Language : En, Es, Fr & De

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Book Description :

Principles and Labs for Deep Learning provides the knowledge and techniques needed to help readers design and develop deep learning models. Deep Learning techniques are introduced through theory, comprehensively illustrated, explained through the TensorFlow source code examples, and analyzed through the visualization of results. The structured methods and labs provided by Dr. Huang and Dr. Le enable readers to become proficient in TensorFlow to build deep Convolutional Neural Networks (CNNs) through custom APIs, high-level Keras APIs, Keras Applications, and TensorFlow Hub. Each chapter has one corresponding Lab with step-by-step instruction to help the reader practice and accomplish a specific learning outcome. Deep Learning has been successfully applied in diverse fields such as computer vision, audio processing, robotics, natural language processing, bioinformatics and chemistry. Because of the huge scope of knowledge in Deep Learning, a lot of time is required to understand and deploy useful, working applications, hence the importance of this new resource. Both theory lessons and experiments are included in each chapter to introduce the techniques and provide source code examples to practice using them. All Labs for this book are placed on GitHub to facilitate the download. The book is written based on the assumption that the reader knows basic Python for programming and basic Machine Learning. Introduces readers to the usefulness of neural networks and Deep Learning methods Provides readers with in-depth understanding of the architecture and operation of Deep Convolutional Neural Networks Demonstrates the visualization needed for designing neural networks Provides readers with an in-depth understanding of regression problems, binary classification problems, multi-category classification problems, Variational Auto-Encoder, Generative Adversarial Network, and Object detection

Principles and Labs for Deep Learning

Principles and Labs for Deep Learning Book
Author : Shih-Chia Huang,Trung-Hieu Le
Publisher : Elsevier
Release : 2021-07-09
ISBN : 0323901980
Language : En, Es, Fr & De

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Book Description :

Principles and Labs for Deep Learning provides the knowledge and techniques needed to help readers design and develop deep learning models. Deep Learning techniques are introduced through theory, comprehensively illustrated, explained through the TensorFlow source code examples, and analyzed through the visualization of results. The structured methods and labs provided by Dr. Huang and Dr. Le enable readers to become proficient in TensorFlow to build deep Convolutional Neural Networks (CNNs) through custom APIs, high-level Keras APIs, Keras Applications, and TensorFlow Hub. Each chapter has one corresponding Lab with step-by-step instruction to help the reader practice and accomplish a specific learning outcome. Deep Learning has been successfully applied in diverse fields such as computer vision, audio processing, robotics, natural language processing, bioinformatics and chemistry. Because of the huge scope of knowledge in Deep Learning, a lot of time is required to understand and deploy useful, working applications, hence the importance of this new resource. Both theory lessons and experiments are included in each chapter to introduce the techniques and provide source code examples to practice using them. All Labs for this book are placed on GitHub to facilitate the download. The book is written based on the assumption that the reader knows basic Python for programming and basic Machine Learning. Introduces readers to the usefulness of neural networks and Deep Learning methods Provides readers with in-depth understanding of the architecture and operation of Deep Convolutional Neural Networks Demonstrates the visualization needed for designing neural networks Provides readers with an in-depth understanding of regression problems, binary classification problems, multi-category classification problems, Variational Auto-Encoder, Generative Adversarial Network, and Object detection

Deep Learning in Computer Vision

Deep Learning in Computer Vision Book
Author : Mahmoud Hassaballah,Ali Ismail Awad
Publisher : CRC Press
Release : 2020-03-23
ISBN : 1351003801
Language : En, Es, Fr & De

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Book Description :

Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions to several image-related problems that had long remained unsolved or partially addressed. This book presents a collection of eleven chapters where each individual chapter explains the deep learning principles of a specific topic, introduces reviews of up-to-date techniques, and presents research findings to the computer vision community. The book covers a broad scope of topics in deep learning concepts and applications such as accelerating the convolutional neural network inference on field-programmable gate arrays, fire detection in surveillance applications, face recognition, action and activity recognition, semantic segmentation for autonomous driving, aerial imagery registration, robot vision, tumor detection, and skin lesion segmentation as well as skin melanoma classification. The content of this book has been organized such that each chapter can be read independently from the others. The book is a valuable companion for researchers, for postgraduate and possibly senior undergraduate students who are taking an advanced course in related topics, and for those who are interested in deep learning with applications in computer vision, image processing, and pattern recognition.

Deep Learning from Scratch

Deep Learning from Scratch Book
Author : Seth Weidman
Publisher : O'Reilly Media
Release : 2019-09-09
ISBN : 1492041386
Language : En, Es, Fr & De

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Book Description :

With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. You’ll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way. Author Seth Weidman shows you how neural networks work using a first principles approach. You’ll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural networks work mathematically, computationally, and conceptually, you’ll be set up for success on all future deep learning projects. This book provides: Extremely clear and thorough mental models—accompanied by working code examples and mathematical explanations—for understanding neural networks Methods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented framework Working implementations and clear-cut explanations of convolutional and recurrent neural networks Implementation of these neural network concepts using the popular PyTorch framework

Feature Engineering for Machine Learning

Feature Engineering for Machine Learning Book
Author : Alice Zheng,Amanda Casari
Publisher : "O'Reilly Media, Inc."
Release : 2018-03-23
ISBN : 1491953195
Language : En, Es, Fr & De

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Book Description :

Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering. Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples. You’ll examine: Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transforms Natural text techniques: bag-of-words, n-grams, and phrase detection Frequency-based filtering and feature scaling for eliminating uninformative features Encoding techniques of categorical variables, including feature hashing and bin-counting Model-based feature engineering with principal component analysis The concept of model stacking, using k-means as a featurization technique Image feature extraction with manual and deep-learning techniques

Deep Learning in Gaming and Animations

Deep Learning in Gaming and Animations Book
Author : Vikas Chaudhary,Moolchand Sharma,Prerna Sharma,Deevyankar Agarwal
Publisher : CRC Press
Release : 2021-12-08
ISBN : 1000504379
Language : En, Es, Fr & De

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Book Description :

Over the last decade, progress in deep learning has had a profound and transformational effect on many complex problems, including speech recognition, machine translation, natural language understanding, and computer vision. As a result, computers can now achieve human-competitive performance in a wide range of perception and recognition tasks. Many of these systems are now available to the programmer via a range of so-called cognitive services. More recently, deep reinforcement learning has achieved ground-breaking success in several complex challenges. This book makes an enormous contribution to this beautiful, vibrant area of study: an area that is developing rapidly both in breadth and depth. Deep learning can cope with a broader range of tasks (and perform those tasks to increasing levels of excellence). This book lays a good foundation for the core concepts and principles of deep learning in gaming and animation, walking you through the fundamental ideas with expert ease. This book progresses in a step-by-step manner. It reinforces theory with a full-fledged pedagogy designed to enhance students' understanding and offer them a practical insight into its applications. Also, some chapters introduce and cover novel ideas about how artificial intelligence (AI), deep learning, and machine learning have changed the world in gaming and animation. It gives us the idea that AI can also be applied in gaming, and there are limited textbooks in this area. This book comprehensively addresses all the aspects of AI and deep learning in gaming. Also, each chapter follows a similar structure so that students, teachers, and industry experts can orientate themselves within the text. There are few books in the field of gaming using AI. Deep Learning in Gaming and Animations teaches you how to apply the power of deep learning to build complex reasoning tasks. After being exposed to the foundations of machine and deep learning, you will use Python to build a bot and then teach it the game's rules. This book also focuses on how different technologies have revolutionized gaming and animation with various illustrations.

Principles and Theory for Data Mining and Machine Learning

Principles and Theory for Data Mining and Machine Learning Book
Author : Bertrand Clarke,Ernest Fokoue,Hao Helen Zhang
Publisher : Springer Science & Business Media
Release : 2009-07-21
ISBN : 0387981357
Language : En, Es, Fr & De

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Book Description :

Extensive treatment of the most up-to-date topics Provides the theory and concepts behind popular and emerging methods Range of topics drawn from Statistics, Computer Science, and Electrical Engineering

Neural Networks with R

Neural Networks with R Book
Author : Giuseppe Ciaburro,Balaji Venkateswaran
Publisher : Packt Publishing Ltd
Release : 2017-09-27
ISBN : 1788399412
Language : En, Es, Fr & De

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Book Description :

Uncover the power of artificial neural networks by implementing them through R code. About This Book Develop a strong background in neural networks with R, to implement them in your applications Build smart systems using the power of deep learning Real-world case studies to illustrate the power of neural network models Who This Book Is For This book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need! What You Will Learn Set up R packages for neural networks and deep learning Understand the core concepts of artificial neural networks Understand neurons, perceptrons, bias, weights, and activation functions Implement supervised and unsupervised machine learning in R for neural networks Predict and classify data automatically using neural networks Evaluate and fine-tune the models you build. In Detail Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning. This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you'll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book. Style and approach A step-by-step guide filled with real-world practical examples.

Deep Learning from First Principles Second Edition

Deep Learning from First Principles  Second Edition Book
Author : Tinniam V. Ganesh
Publisher : Unknown
Release : 2018-12-13
ISBN : 9781791596170
Language : En, Es, Fr & De

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Book Description :

This is the second edition of the book. The code has been formatted with fixed with a fixed width font, and includes line numbering. This book derives and builds a multi-layer, multi-unit Deep Learning from the basics. The first chapter starts with the derivation and implementation of Logistic Regression as a Neural Network. This followed by building a generic L-Layer Deep Learning Network which performs binary classification. This Deep Learning network is then enhanced to handle multi-class classification along with the necessary derivations for the Jacobian of softmax and cross-entropy loss. Further chapters include different initialization types, regularization methods (L2, dropout) followed by gradient descent optimization techniques like Momentum, Rmsprop and Adam. Finally the technique of gradient checking is elaborated and implemented. All the chapters include implementations in vectorized Python, R and Octave. Detailed derivations are included for each critical enhancement to the Deep Learning. By the time you reach the last chapter, the implementation includes fully functional L-Layer Deep Learning with all the bells and whistles in vectorized Python, R and Octave. The code, for all the chapters, has been included in the Appendix section

The Principles of Deep Learning Theory

The Principles of Deep Learning Theory Book
Author : Daniel A. Roberts,Sho Yaida,Boris Hanin
Publisher : Cambridge University Press
Release : 2022-05-26
ISBN : 1316519333
Language : En, Es, Fr & De

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Book Description :

This volume develops an effective theory approach to understanding deep neural networks of practical relevance.

Principles Of Artificial Neural Networks Basic Designs To Deep Learning 4th Edition

Principles Of Artificial Neural Networks  Basic Designs To Deep Learning  4th Edition  Book
Author : Graupe Daniel
Publisher : World Scientific
Release : 2019-03-15
ISBN : 9811201242
Language : En, Es, Fr & De

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Book Description :

The field of Artificial Neural Networks is the fastest growing field in Information Technology and specifically, in Artificial Intelligence and Machine Learning.This must-have compendium presents the theory and case studies of artificial neural networks. The volume, with 4 new chapters, updates the earlier edition by highlighting recent developments in Deep-Learning Neural Networks, which are the recent leading approaches to neural networks. Uniquely, the book also includes case studies of applications of neural networks — demonstrating how such case studies are designed, executed and how their results are obtained.The title is written for a one-semester graduate or senior-level undergraduate course on artificial neural networks. It is also intended to be a self-study and a reference text for scientists, engineers and for researchers in medicine, finance and data mining.

Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques Tools and Applications

Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques  Tools  and Applications Book
Author : K. G. Srinivasa,G. M. Siddesh,S. R. Manisekhar
Publisher : Springer Nature
Release : 2020-01-30
ISBN : 9811524459
Language : En, Es, Fr & De

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Book Description :

This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics. It also highlights the role of computing and machine learning in knowledge extraction from biological data, and how this knowledge can be applied in fields such as drug design, health supplements, gene therapy, proteomics and agriculture.

Principles Of Quantum Artificial Intelligence Quantum Problem Solving And Machine Learning Second Edition

Principles Of Quantum Artificial Intelligence  Quantum Problem Solving And Machine Learning  Second Edition  Book
Author : Andreas Miroslaus Wichert
Publisher : World Scientific
Release : 2020-07-08
ISBN : 9811224323
Language : En, Es, Fr & De

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Book Description :

This unique compendium presents an introduction to problem solving, information theory, statistical machine learning, stochastic methods and quantum computation. It indicates how to apply quantum computation to problem solving, machine learning and quantum-like models to decision making — the core disciplines of artificial intelligence.Most of the chapters were rewritten and extensive new materials were updated. New topics include quantum machine learning, quantum-like Bayesian networks and mind in Everett many-worlds.

Text Mining with Machine Learning

Text Mining with Machine Learning Book
Author : Jan Žižka,František Dařena,Arnošt Svoboda
Publisher : CRC Press
Release : 2019-11-20
ISBN : 0429890265
Language : En, Es, Fr & De

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Book Description :

This book provides a perspective on the application of machine learning-based methods in knowledge discovery from natural languages texts. By analysing various data sets, conclusions which are not normally evident, emerge and can be used for various purposes and applications. The book provides explanations of principles of time-proven machine learning algorithms applied in text mining together with step-by-step demonstrations of how to reveal the semantic contents in real-world datasets using the popular R-language with its implemented machine learning algorithms. The book is not only aimed at IT specialists, but is meant for a wider audience that needs to process big sets of text documents and has basic knowledge of the subject, e.g. e-mail service providers, online shoppers, librarians, etc. The book starts with an introduction to text-based natural language data processing and its goals and problems. It focuses on machine learning, presenting various algorithms with their use and possibilities, and reviews the positives and negatives. Beginning with the initial data pre-processing, a reader can follow the steps provided in the R-language including the subsuming of various available plug-ins into the resulting software tool. A big advantage is that R also contains many libraries implementing machine learning algorithms, so a reader can concentrate on the principal target without the need to implement the details of the algorithms her- or himself. To make sense of the results, the book also provides explanations of the algorithms, which supports the final evaluation and interpretation of the results. The examples are demonstrated using realworld data from commonly accessible Internet sources.

Machine Learning

Machine Learning Book
Author : Alexander Jung
Publisher : Springer
Release : 2022-03-14
ISBN : 9789811681929
Language : En, Es, Fr & De

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Book Description :

Machine learning (ML) has become a commonplace element in our everyday lives and a standard tool for many fields of science and engineering. To make optimal use of ML, it is essential to understand its underlying principles. This book approaches ML as the computational implementation of the scientific principle. This principle consists of continuously adapting a model of a given data-generating phenomenon by minimizing some form of loss incurred by its predictions. The book trains readers to break down various ML applications and methods in terms of data, model, and loss, thus helping them to choose from the vast range of ready-made ML methods. The book’s three-component approach to ML provides uniform coverage of a wide range of concepts and techniques. As a case in point, techniques for regularization, privacy-preservation as well as explainability amount to specific design choices for the model, data, and loss of a ML method.

Machine Learning and Data Mining

Machine Learning and Data Mining Book
Author : Igor Kononenko,Matjaz Kukar
Publisher : Horwood Publishing
Release : 2007-05-14
ISBN : 9781904275213
Language : En, Es, Fr & De

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Book Description :

Good data mining practice for business intelligence (the art of turning raw software into meaningful information) is demonstrated by the many new techniques and developments in the conversion of fresh scientific discovery into widely accessible software solutions. Written as an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining, this text is suitable foradvanced undergraduates, postgraduates and tutors in a wide area of computer science and technology, as well as researchers looking to adapt various algorithms for particular data mining tasks. A valuable addition to libraries and bookshelves of the many companies who are using the principles of data mining to effectively deliver solid business and industry solutions.

Mathematics for Machine Learning

Mathematics for Machine Learning Book
Author : Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong
Publisher : Cambridge University Press
Release : 2020-03-31
ISBN : 1108470041
Language : En, Es, Fr & De

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Book Description :

Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.

Dive Into Deep Learning

Dive Into Deep Learning Book
Author : Joanne Quinn,Joanne McEachen,Michael Fullan,Mag Gardner,Max Drummy
Publisher : Corwin Press
Release : 2019-07-15
ISBN : 1544385404
Language : En, Es, Fr & De

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Book Description :

The leading experts in system change and learning, with their school-based partners around the world, have created this essential companion to their runaway best-seller, Deep Learning: Engage the World Change the World. This hands-on guide provides a roadmap for building capacity in teachers, schools, districts, and systems to design deep learning, measure progress, and assess conditions needed to activate and sustain innovation. Dive Into Deep Learning: Tools for Engagement is rich with resources educators need to construct and drive meaningful deep learning experiences in order to develop the kind of mindset and know-how that is crucial to becoming a problem-solving change agent in our global society. Designed in full color, this easy-to-use guide is loaded with tools, tips, protocols, and real-world examples. It includes: • A framework for deep learning that provides a pathway to develop the six global competencies needed to flourish in a complex world — character, citizenship, collaboration, communication, creativity, and critical thinking. • Learning progressions to help educators analyze student work and measure progress. • Learning design rubrics, templates and examples for incorporating the four elements of learning design: learning partnerships, pedagogical practices, learning environments, and leveraging digital. • Conditions rubrics, teacher self-assessment tools, and planning guides to help educators build, mobilize, and sustain deep learning in schools and districts. Learn about, improve, and expand your world of learning. Put the joy back into learning for students and adults alike. Dive into deep learning to create learning experiences that give purpose, unleash student potential, and transform not only learning, but life itself.

Deep Learning in Science

Deep Learning in Science Book
Author : Pierre Baldi
Publisher : Cambridge University Press
Release : 2021-07
ISBN : 1108845355
Language : En, Es, Fr & De

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Book Description :

Rigorous treatment of the theory of deep learning from first principles, with applications to beautiful problems in the natural sciences.

FIRST Framework 5 Domains 15 Principles Design Facilitate Active Deep Learner eXperience Volume 1 1st Edition

FIRST Framework  5 Domains 15 Principles  Design   Facilitate Active Deep Learner eXperience  Volume 1  1st Edition Book
Author : Mohamed M. Bahgat
Publisher : Mohamed M. Bahgat
Release : 2018-02-08
ISBN : 1984934260
Language : En, Es, Fr & De

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Book Description :

No doubt learning is a subject that has been addressed by many books and workshops, with the core interest mostly revolving around the content and how to make it unique, relevant, concise, etc. Other books and courses would rather introduce new/creative techniques for better engaging or getting the best of the training. So where does this book stand? This book stands in the learner's shoes! It is standing as a cornerstone for a different approach, having an eye for every detail that might reflect on the learner's experience; hence the name, "Learner eXperience Facilitation". Who is this book for? This book is for learning facilitators and designers, so to get introduced to a new perspective and to learn new framework where the learner is the center of the whole process. This is considered a sound tool for professionals who work hard to leave an impact through trainings and face to face learning sessions. It targets both independent professionals and those working for specific organizations, teachers and education professionals. This book introduces FIRST framework, research based framework, which is holistic and backed by theories from different basic sciences like, educational psychology, neuroscience, cognitive psychology, design thinking; among others. These disciplines are combined together so to create an engaging framework; leading to creating positive Active Deep learner experience, and hence, positive change in mindset and behaviors. If you are a learning facilitator and you feel the need of a creative and innovative framework to highly influence trainees, then this book is for you; through which you add the active deep learning techniques to your facilitation style. FIRST framework This book introduces FIRST framework, which includes five main domains, and 15 principles. These principles act together and integrate together; creating the Active Deep Learner eXperience. FIRST framework is a holistic one; it is based on other models and theories, such as: experiential learning by Kolb and John Dewey; cooperative learning by Kagan; Carl Rogers’ facilitation skills, Roy’s 6Ds and learning transfer; as well as positive psychology principles. FIRST is also inspired by the spirit of group coaching, which aims at promoting deep change and is focused on the future. It is a scientific and research based framework, developed through our experience in learning and development field; as well as measuring the impact of implementing the model via SeGa or our learners. FIRST framework is not only aiming to create active learning experience, it also targets transforming learning into performance, because incorporating both active and deep strategies creates engagement and impact. The five domains of FIRST act as layers each of them build on the previous domain and add to it, all the principles integrate with each other to form the active deep learner experience. "The end result is a proven, practical, and priceless model with five domains and fifteen principles that you can use yourself in staging active deep learning experiences." B. Joseph Pine II. Author, The Experience Economy. "FIRST framework provides important insights, principles, and practical advice for doing so a travel guide, if you will, that will help you on your own learning journey and those on which you lead others." Roy V.H. Pollock, DVM, PhD. Author, The Six Disciplines of Breakthrough Learning