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Deep Learning In A Disorienting World
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Book Synopsis Deep Learning in a Disorienting World by : Jon F. Wergin
Download or read book Deep Learning in a Disorienting World written by Jon F. Wergin and published by Cambridge University Press. This book was released on 2020 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: Shows how deep learning is a way to address the toxicity of social polarization.
Download or read book Deep Learning written by Ian Goodfellow and published by MIT Press. This book was released on 2016-11-18 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Drawing on Students’ Worlds in the ELA Classroom by : Richard Beach
Download or read book Drawing on Students’ Worlds in the ELA Classroom written by Richard Beach and published by Routledge. This book was released on 2022-04-21 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book approaches English instruction through the lens of “fi gured worlds,” which recognizes and spotlights how students are actively engaged in constructing their own school, peer group, extracurricular, and community worlds. Teachers’ ability not only to engage with students’ experiences and interests in and outside of school but also to build connections between students’ worlds and their teaching is essential for promoting student agency, engagement, and meaningful learning. Beach and Caraballo provide an accessible framework for working with students to use critical discourse, narratives, media, genres, and more to support their identity development through addressing topics that are meaningful for them— their families, social issues, virtual worlds, and more. Through extensive activities and examples of students writing about their participation in these worlds, this text allows educators to recognize how students’ experiences in the classroom aff ect and shape their identities and to connect such an understanding to successful classroom practice. With chapters featuring eff ective instructional activities, this book is necessary reading for ELA methods courses and for all English teachers.
Book Synopsis Multi-faceted Deep Learning by : Jenny Benois-Pineau
Download or read book Multi-faceted Deep Learning written by Jenny Benois-Pineau and published by Springer Nature. This book was released on 2021-10-20 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers a large set of methods in the field of Artificial Intelligence - Deep Learning applied to real-world problems. The fundamentals of the Deep Learning approach and different types of Deep Neural Networks (DNNs) are first summarized in this book, which offers a comprehensive preamble for further problem–oriented chapters. The most interesting and open problems of machine learning in the framework of Deep Learning are discussed in this book and solutions are proposed. This book illustrates how to implement the zero-shot learning with Deep Neural Network Classifiers, which require a large amount of training data. The lack of annotated training data naturally pushes the researchers to implement low supervision algorithms. Metric learning is a long-term research but in the framework of Deep Learning approaches, it gets freshness and originality. Fine-grained classification with a low inter-class variability is a difficult problem for any classification tasks. This book presents how it is solved, by using different modalities and attention mechanisms in 3D convolutional networks. Researchers focused on Machine Learning, Deep learning, Multimedia and Computer Vision will want to buy this book. Advanced level students studying computer science within these topic areas will also find this book useful.
Download or read book Deep Learning written by Andrew Glassner and published by No Starch Press. This book was released on 2021-06-22 with total page 1239 pages. Available in PDF, EPUB and Kindle. Book excerpt: A richly-illustrated, full-color introduction to deep learning that offers visual and conceptual explanations instead of equations. You'll learn how to use key deep learning algorithms without the need for complex math. Ever since computers began beating us at chess, they've been getting better at a wide range of human activities, from writing songs and generating news articles to helping doctors provide healthcare. Deep learning is the source of many of these breakthroughs, and its remarkable ability to find patterns hiding in data has made it the fastest growing field in artificial intelligence (AI). Digital assistants on our phones use deep learning to understand and respond intelligently to voice commands; automotive systems use it to safely navigate road hazards; online platforms use it to deliver personalized suggestions for movies and books - the possibilities are endless. Deep Learning: A Visual Approach is for anyone who wants to understand this fascinating field in depth, but without any of the advanced math and programming usually required to grasp its internals. If you want to know how these tools work, and use them yourself, the answers are all within these pages. And, if you're ready to write your own programs, there are also plenty of supplemental Python notebooks in the accompanying Github repository to get you going. The book's conversational style, extensive color illustrations, illuminating analogies, and real-world examples expertly explain the key concepts in deep learning, including: • How text generators create novel stories and articles • How deep learning systems learn to play and win at human games • How image classification systems identify objects or people in a photo • How to think about probabilities in a way that's useful to everyday life • How to use the machine learning techniques that form the core of modern AI Intellectual adventurers of all kinds can use the powerful ideas covered in Deep Learning: A Visual Approach to build intelligent systems that help us better understand the world and everyone who lives in it. It's the future of AI, and this book allows you to fully envision it. Full Color Illustrations
Book Synopsis Neural Networks and Deep Learning by : Pat Nakamoto
Download or read book Neural Networks and Deep Learning written by Pat Nakamoto and published by Createspace Independent Publishing Platform. This book was released on 2018-06-30 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: What's Inside? This includes 3 manuscripts: Book 1: Neural Networks & Deep Learning: Deep Learning explained to your granny - A visual introduction for beginners who want to make their own Deep Learning Neural Network... What you will gain from this book: * A deep understanding of how Deep Learning works * A basics comprehension on how to build a Deep Neural Network from scratch Who this book is for: * Beginners who want to approach the topic, but are too afraid of complex math to start! * Two main Types of Machine Learning Algorithms * A practical example of Unsupervised Learning * What are Neural Networks? * McCulloch-Pitts's Neuron * Types of activation function * Types of network architectures * Learning processes * Advantages and disadvantages * Let us give a memory to our Neural Network * The example of book writing Software * Deep learning: the ability of learning to learn * How does Deep Learning work? * Main architectures and algorithms * Main types of DNN * Available Frameworks and libraries * Convolutional Neural Networks * Tunnel Vision * Convolution * The right Architecture for a Neural Network * Test your Neural Network * A general overview of Deep Learning * What are the limits of Deep Learning? * Deep Learning: the basics * Layers, Learning paradigms, Training, Validation * Main architectures and algorithms * Models for Deep Learning * Probabilistic graphic models * Restricted Boltzmann Machines * Deep Belief Networks Book2: Deep Learning: Deep Learning explained to your granny - A guide for Beginners... What's Inside? * A general overview of Deep Learning * What are the limits of Deep Learning? * Deep Learning: the basics * Layers, Learning paradigms, Training, Validation * Main architectures and algorithms * Convolutional Neural Networks * Models for Deep Learning * Probabilistic graphic models * Restricted Boltzmann Machines * Deep Belief Networks * Available Frameworks and libraries * TensorFlow Book 3: Big Data: The revolution that is transforming our work, market and world... "Within 2 days we produce the same amount of data generated by at the beginning of the civilization until 2003," said Eric Schmidt in 2010. According to IBM, by 2020 the world will have generated a mass of data on the order of 40 zettabyte (1021Byte). Just think, for example, of digital content such as photos, videos, blogs, posts, and everything that revolves around social networks; only Facebook marks 30 billion pieces of content each month shared by its users. The explosion of social networks, combined with the emergence of smartphones, justifies the fact that one of the recurring terms of recent years in the field of innovation, marketing and IT is "Big Data." The term Big Data indicates data produced in massive quantities, with remarkable rapidity and in the most diverse formats, which require technologies and resources that go far beyond conventional data management and storage systems. In order to obtain from the use of this data the maximum results in the shortest possible time or even in real time, specific tools with high computing capabilities are necessary. But what does the Big Data phenomenon mean? Is the proliferation of data simply the sign of an increasingly invasive world? Or is there something more to it? Pat Nakamoto will guide you through the discovery of the world of Big data, which, according to experts, in the near future could become the new gold or oil, in what is a real Data Driven economy.
Download or read book Deep Learning written by Dulani Meedeniya and published by CRC Press. This book was released on 2023-10-16 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on deep learning (DL), which is an important aspect of data science, that includes predictive modeling. DL applications are widely used in domains such as finance, transport, healthcare, automanufacturing, and advertising. The design of the DL models based on artificial neural networks is influenced by the structure and operation of the brain. This book presents a comprehensive resource for those who seek a solid grasp of the techniques in DL. Key features: • Provides knowledge on theory and design of state-of-the-art deep learning models for real-world applications. • Explains the concepts and terminology in problem-solving with deep learning. • Explores the theoretical basis for major algorithms and approaches in deep learning. • Discusses the enhancement techniques of deep learning models. • Identifies the performance evaluation techniques for deep learning models. Accordingly, the book covers the entire process flow of deep learning by providing awareness of each of the widely used models. This book can be used as a beginners’ guide where the user can understand the associated concepts and techniques. This book will be a useful resource for undergraduate and postgraduate students, engineers, and researchers, who are starting to learn the subject of deep learning.
Book Synopsis Discovering Leadership by : Anthony Middlebrooks
Download or read book Discovering Leadership written by Anthony Middlebrooks and published by SAGE Publications. This book was released on 2023-02-28 with total page 605 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discovering Leadership: Designing Your Success, Second Edition provides a comprehensive practice-based introduction to leadership. The text guides students to design their leadership purposefully and mindfully as they develop their leadership knowledge, skills, and dispositions.
Book Synopsis Generative Knowing by : Aliki Nicolaides
Download or read book Generative Knowing written by Aliki Nicolaides and published by Myers Education Press. This book was released on 2022-09-27 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: A 2023 SPE Outstanding Book Honorable Mention Generative Knowing explores the mystery of learning from the unknown in ways that reveal that learning is a dynamic phenomenon, encompassing both personal and societal contexts. Dewey defines learning in terms of experience, reflection, continuity, and interactivity. When learning happens, it eventually solidifies into reliable truths that become a shortcut for taking action or making decisions—thus a habit of learning is formed and becomes rigid. Generative knowing is an emerging theory of adult learning that seeks the not-yet-foreknown potential that waits to be uncovered in the richness of experience. The book delivers vignettes of different lived experiences of being and becoming, signaling multiple ways in which a person shapes and transcends traditional conceptions of self-other binary activating the power to respond to the ongoing complex evolution of self and society. Generative Knowing seeks to accomplish four goals: to offer a unique exploration of learning, positioned as response-ability that illuminates the relatedness of learning and complex, ambiguous, unsolvable challenges that are recognizable in society as social challenges (i.e. forced migration) to present and distinguish an emerging theory of adult learning, generative knowing. Generative knowing emerged as a distinct learning disposition at the intersections of personal meaning making capacity (developmental psychology) encountering the characteristics of rising ambiguity (complexity sciences) and the lived experience of undergoing experience to make visible and help others make the connections between generative knowing at a personal level and the complex, ambiguous unsolvable challenges in today’s society, and to provide illustrations of what generative knowing entails, how it shapes personal and societal transformation and how that may support educators, facilitator activists and change activists to make space for generative knowing when complex challenges call for both personal and societal transformations. Adult education as a field of practice is presently grappling with how adults learn in a world being recomposed by a global pandemic. Generative knowing—defined as ways of being and becoming that creatively activate potential—restores many rhythms of learning, helping readers gain fresh perspectives on how learning emerges from the unknown. The vital and personal stories in this book guide readers to walk in the territory of the unknown and to pay attention to the sensations of entanglements of self with multiple societal forces as a new way of learning. Perfect for courses such as: Adult Learning Theory │ Adult Learning Theory & Praxis │ Adult Development │ Transformative Learning │ Phenomenology │ Narrative Inquiry │ New Materialism │ Creative Research Methodologies
Book Synopsis Generative Deep Learning by : David Foster
Download or read book Generative Deep Learning written by David Foster and published by "O'Reilly Media, Inc.". This book was released on 2022-06-28 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy-based models, and denoising diffusion models. The book starts with the basics of deep learning and progresses to cutting-edge architectures. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative. Discover how VAEs can change facial expressions in photos Train GANs to generate images based on your own dataset Build diffusion models to produce new varieties of flowers Train your own GPT for text generation Learn how large language models like ChatGPT are trained Explore state-of-the-art architectures such as StyleGAN2 and ViT-VQGAN Compose polyphonic music using Transformers and MuseGAN Understand how generative world models can solve reinforcement learning tasks Dive into multimodal models such as DALL.E 2, Imagen, and Stable Diffusion This book also explores the future of generative AI and how individuals and companies can proactively begin to leverage this remarkable new technology to create competitive advantage.
Book Synopsis The Routledge Companion to Leadership and Change by : Satinder K. Dhiman
Download or read book The Routledge Companion to Leadership and Change written by Satinder K. Dhiman and published by Taylor & Francis. This book was released on 2023-03-28 with total page 657 pages. Available in PDF, EPUB and Kindle. Book excerpt: The unique leadership challenges organizations face throughout the world call for a renewed focus on what constitutes "authentic, inclusive, servant, transformational, principled, values-based, and mindful" leadership. Traditional approaches rarely provide a permeating or systematic framework to garner a sense of higher purpose or nurture deeper moral and spiritual dimensions of leaders. Learning to be an effective leader requires a deep personal transformation, which is not easy. This text provides guidelines in a variety of settings and contexts while presenting best practices in successfully leading the twenty-first century workforce and offering strategies and tools to lead change effectively in the present-day boundary-less work environment. Given the ever-growing, widespread importance of leadership and its role in initiating change, this will be a key reference work in the field of leadership and change management in business. The uniqueness of this book lies in its anchorage in the moral and spiritual dimension of leadership, an approach most relevant for contemporary times and organizations. It represents an important milestone in the perennial quest for discovering the best leadership models and change practices to suit the contemporary organizations. Designed to be a resource for scholars, practitioners, teachers and students seeking guidance in the art and science of leadership and change management, this will be an invaluable reference for libraries with collections in business, management, sports, history, politics, law, and psychology. It will present essential strategies for leading and transforming corporations, small businesses, schools, hospitals, and various nonprofit organizations. It brings the research on leadership and change management up to date, while mapping its terrain and extending the scope and boundaries of this field in an inclusive and egalitarian manner.
Book Synopsis Neural Networks and Deep Learning by : Pat Nakamoto
Download or read book Neural Networks and Deep Learning written by Pat Nakamoto and published by Createspace Independent Publishing Platform. This book was released on 2018-01-12 with total page 102 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ready to crank up a neural network to get your self-driving car pick up the kids from school? Want to add 'Deep Learning' to your LinkedIn profile? Well, hold on there... Before you embark on your epic journey into the world of deep learning, there is basic theory to march through first! Take a step-by-step journey through the basics of Neural Networks and Deep Learning, made so simple that...even your granny could understand it! What you will gain from this book: * A deep understanding of how a Neural Network and Deep Learning work * A basics comprehension on how to build a Deep Neural Network from scratch Who this book is for: * Beginners who want to approach the topic, but are too afraid of complex math to start! What's Inside? * A brief introduction to Machine Learning * Two main Types of Machine Learning Algorithms * A practical example of Unsupervised Learning * What are Neural Networks? * McCulloch-Pitts's Neuron * Types of activation function * Types of network architectures * Learning processes * Advantages and disadvantages * Let us give a memory to our Neural Network * The example of book writing Software * Deep learning: the ability of learning to learn * How does Deep Learning work? * Main architectures and algorithms * Main types of DNN * Available Frameworks and libraries * Convolutional Neural Networks * Tunnel Vision * Convolution * The right Architecture for a Neural Network * Test your Neural Network * A general overview of Deep Learning * What are the limits of Deep Learning? * Deep Learning: the basics * Layers, Learning paradigms, Training, Validation * Main architectures and algorithms * Models for Deep Learning * Probabilistic graphic models * Restricted Boltzmann Machines * Deep Belief Networks * Available Frameworks and libraries * TensorFlow Hit download. Now!
Book Synopsis Inclusive Leadership by : Joanne Barnes
Download or read book Inclusive Leadership written by Joanne Barnes and published by Emerald Group Publishing. This book was released on 2023-10-16 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Inclusive Leadership speaks to the human side of organization and communities. Both practitioners and academics provide insights that broaden our traditional view of diversity issues into a perspective focused on better understanding the theory and practice of inclusive leadership.
Book Synopsis Adaptive Leadership in a Global Economy by : Mohammed Raei
Download or read book Adaptive Leadership in a Global Economy written by Mohammed Raei and published by Routledge. This book was released on 2021-12-28 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the entire world experiencing the global pandemic and its aftermath, VUCA (Volatile, Uncertain, Complex, and Ambiguous) conditions have never been more extreme and the need for adaptive leadership never more urgent. But how is adaptive leadership applied outside Western cultures? How can it be taught through leadership development programs? Which tools enhance its practice and its teaching? How does adaptive leadership relate to other key theories and practices? This volume answers these questions and more as it illustrates how adaptive leadership practices address some of the world’s most pressing challenges-political and cultural division, remote work, crisis management-across a variety of sectors. Adaptive leadership has been explained as a key leadership approach for dealing with adaptive, as distinguished from technical or predictable, problems, especially prevalent in complex environments. However, adaptive leadership scholarship has suffered from a lack of conceptual clarity and casual application of its core concepts. It remains solidly Western in its prescriptions. This book will expand readers’ understanding of adaptive leadership and its potential to solve local and global adaptive challenges and will explore its relevance and application to cultures outside the United States. Aiming to increase conceptual clarity about adaptive leadership to enhance future scholarship and application and illustrate novel approaches and perspectives, this book will be of interest to researchers, academics, practitioners, and students in the fields of leadership, strategy, and organizational studies.
Book Synopsis Deep Learning For Eeg-based Brain-computer Interfaces: Representations, Algorithms And Applications by : Xiang Zhang
Download or read book Deep Learning For Eeg-based Brain-computer Interfaces: Representations, Algorithms And Applications written by Xiang Zhang and published by World Scientific. This book was released on 2021-09-14 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning for EEG-Based Brain-Computer Interfaces is an exciting book that describes how emerging deep learning improves the future development of Brain-Computer Interfaces (BCI) in terms of representations, algorithms and applications. BCI bridges humanity's neural world and the physical world by decoding an individuals' brain signals into commands recognizable by computer devices.This book presents a highly comprehensive summary of commonly-used brain signals; a systematic introduction of around 12 subcategories of deep learning models; a mind-expanding summary of 200+ state-of-the-art studies adopting deep learning in BCI areas; an overview of a number of BCI applications and how deep learning contributes, along with 31 public BCI data sets. The authors also introduce a set of novel deep learning algorithms aimed at current BCI challenges such as robust representation learning, cross-scenario classification, and semi-supervised learning. Various real-world deep learning-based BCI applications are proposed and some prototypes are presented. The work contained within proposes effective and efficient models which will provide inspiration for people in academia and industry who work on BCI.Related Link(s)
Book Synopsis The Deep Learning Revolution by : Terrence J. Sejnowski
Download or read book The Deep Learning Revolution written by Terrence J. Sejnowski and published by MIT Press. This book was released on 2018-10-23 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: How deep learning—from Google Translate to driverless cars to personal cognitive assistants—is changing our lives and transforming every sector of the economy. The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy. Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.
Book Synopsis Deep Learning Foundations by : Taeho Jo
Download or read book Deep Learning Foundations written by Taeho Jo and published by Springer. This book was released on 2023-07-27 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a conceptual understanding of deep learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for studying deep learning algorithms. The second part deals with modification of existing machine learning algorithms into deep learning algorithms. The book’s third part deals with deep neural networks, such as Multiple Perceptron, Recurrent Networks, Restricted Boltzmann Machine, and Convolutionary Neural Networks. The last part provides deep learning techniques that are specialized for text mining tasks. The book is relevant for researchers, academics, students, and professionals in machine learning.
