Feature Learning And Understanding

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Feature Learning and Understanding

Haitao Zhao,Zhihui Lai,Henry Leung,Xianyi Zhang
Feature Learning and Understanding Book PDF

Author : Haitao Zhao,Zhihui Lai,Henry Leung,Xianyi Zhang
Publisher : Springer Nature
Page : 299 pages
File Size : 48,6 Mb
Release : 2020-04-03
Category : Science
ISBN : 9783030407940

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Feature Learning and Understanding by Haitao Zhao,Zhihui Lai,Henry Leung,Xianyi Zhang Pdf

This book covers the essential concepts and strategies within traditional and cutting-edge feature learning methods thru both theoretical analysis and case studies. Good features give good models and it is usually not classifiers but features that determine the effectiveness of a model. In this book, readers can find not only traditional feature learning methods, such as principal component analysis, linear discriminant analysis, and geometrical-structure-based methods, but also advanced feature learning methods, such as sparse learning, low-rank decomposition, tensor-based feature extraction, and deep-learning-based feature learning. Each feature learning method has its own dedicated chapter that explains how it is theoretically derived and shows how it is implemented for real-world applications. Detailed illustrated figures are included for better understanding. This book can be used by students, researchers, and engineers looking for a reference guide for popular methods of feature learning and machine intelligence.

Applied Deep Learning

Umberto Michelucci
Applied Deep Learning Book PDF

Author : Umberto Michelucci
Publisher : Apress
Page : 425 pages
File Size : 46,8 Mb
Release : 2018-09-07
Category : Computers
ISBN : 9781484237908

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Applied Deep Learning by Umberto Michelucci Pdf

Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and Swish), seeing how to perform linear and logistic regression using TensorFlow, and choosing the right cost function. The next section talks about more complicated neural network architectures with several layers and neurons and explores the problem of random initialization of weights. An entire chapter is dedicated to a complete overview of neural network error analysis, giving examples of solving problems originating from variance, bias, overfitting, and datasets coming from different distributions. Applied Deep Learning also discusses how to implement logistic regression completely from scratch without using any Python library except NumPy, to let you appreciate how libraries such as TensorFlow allow quick and efficient experiments. Case studies for each method are included to put into practice all theoretical information. You’ll discover tips and tricks for writing optimized Python code (for example vectorizing loops with NumPy). What You Will Learn Implement advanced techniques in the right way in Python and TensorFlow Debug and optimize advanced methods (such as dropout and regularization) Carry out error analysis (to realize if one has a bias problem, a variance problem, a data offset problem, and so on) Set up a machine learning project focused on deep learning on a complex dataset Who This Book Is For Readers with a medium understanding of machine learning, linear algebra, calculus, and basic Python programming.

Deep Learning

Ian Goodfellow,Yoshua Bengio,Aaron Courville
Deep Learning Book PDF

Author : Ian Goodfellow,Yoshua Bengio,Aaron Courville
Publisher : MIT Press
Page : 801 pages
File Size : 40,8 Mb
Release : 2016-11-10
Category : Computers
ISBN : 9780262337373

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Deep Learning by Ian Goodfellow,Yoshua Bengio,Aaron Courville Pdf

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.

The Principles of Deep Learning Theory

Daniel A. Roberts,Sho Yaida,Boris Hanin
The Principles of Deep Learning Theory Book PDF

Author : Daniel A. Roberts,Sho Yaida,Boris Hanin
Publisher : Cambridge University Press
Page : 473 pages
File Size : 50,6 Mb
Release : 2022-05-26
Category : Computers
ISBN : 9781316519332

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The Principles of Deep Learning Theory by Daniel A. Roberts,Sho Yaida,Boris Hanin Pdf

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

Understanding Deep Learning

Chitta Ranjan, PH D
Understanding Deep Learning Book PDF

Author : Chitta Ranjan, PH D
Publisher : Unknown
Page : 428 pages
File Size : 40,9 Mb
Release : 2020-12-26
Category : Electronic
ISBN : 9798586701947

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Understanding Deep Learning by Chitta Ranjan, PH D Pdf

Think of deep learning as an art of cooking. One way to cook is to follow a recipe. But when we learn how the food, the spices, and the fire behave, we make our creation. And an understanding of the "how" transcends the creation. Likewise, an understanding of the "how" transcends deep learning. In this spirit, this book presents the deep learning constructs, their fundamentals, and how they behave. Baseline models are developed alongside, and concepts to improve them are exemplified.

Multimodal Scene Understanding

Michael Yang,Bodo Rosenhahn,Vittorio Murino
Multimodal Scene Understanding Book PDF

Author : Michael Yang,Bodo Rosenhahn,Vittorio Murino
Publisher : Academic Press
Page : 422 pages
File Size : 52,5 Mb
Release : 2019-07-16
Category : Computers
ISBN : 9780128173596

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Multimodal Scene Understanding by Michael Yang,Bodo Rosenhahn,Vittorio Murino Pdf

Multimodal Scene Understanding: Algorithms, Applications and Deep Learning presents recent advances in multi-modal computing, with a focus on computer vision and photogrammetry. It provides the latest algorithms and applications that involve combining multiple sources of information and describes the role and approaches of multi-sensory data and multi-modal deep learning. The book is ideal for researchers from the fields of computer vision, remote sensing, robotics, and photogrammetry, thus helping foster interdisciplinary interaction and collaboration between these realms. Researchers collecting and analyzing multi-sensory data collections – for example, KITTI benchmark (stereo+laser) - from different platforms, such as autonomous vehicles, surveillance cameras, UAVs, planes and satellites will find this book to be very useful. Contains state-of-the-art developments on multi-modal computing Shines a focus on algorithms and applications Presents novel deep learning topics on multi-sensor fusion and multi-modal deep learning

Graph Representation Learning

William L. William L. Hamilton
Graph Representation Learning Book PDF

Author : William L. William L. Hamilton
Publisher : Springer Nature
Page : 141 pages
File Size : 45,5 Mb
Release : 2022-06-01
Category : Computers
ISBN : 9783031015885

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Graph Representation Learning by William L. William L. Hamilton Pdf

Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

Dive Into Deep Learning

Joanne Quinn,Joanne McEachen,Michael Fullan,Mag Gardner,Max Drummy
Dive Into Deep Learning Book PDF

Author : Joanne Quinn,Joanne McEachen,Michael Fullan,Mag Gardner,Max Drummy
Publisher : Corwin Press
Page : 297 pages
File Size : 42,5 Mb
Release : 2019-07-15
Category : Education
ISBN : 9781544385402

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Dive Into Deep Learning by Joanne Quinn,Joanne McEachen,Michael Fullan,Mag Gardner,Max Drummy Pdf

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.

Neural Networks and Deep Learning

Charu C. Aggarwal
Neural Networks and Deep Learning Book PDF

Author : Charu C. Aggarwal
Publisher : Springer
Page : 497 pages
File Size : 55,8 Mb
Release : 2018-08-25
Category : Computers
ISBN : 9783319944630

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Neural Networks and Deep Learning by Charu C. Aggarwal Pdf

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.

Deep Learning

Andrew Glassner
Deep Learning Book PDF

Author : Andrew Glassner
Publisher : No Starch Press
Page : 1239 pages
File Size : 51,7 Mb
Release : 2021-06-22
Category : Computers
ISBN : 9781718500730

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Deep Learning by Andrew Glassner Pdf

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

Understanding Deep Learning

Simon J.D. Prince
Understanding Deep Learning Book PDF

Author : Simon J.D. Prince
Publisher : MIT Press
Page : 544 pages
File Size : 44,7 Mb
Release : 2023-12-05
Category : Computers
ISBN : 9780262048644

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Understanding Deep Learning by Simon J.D. Prince Pdf

An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice. Deep learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics. Up-to-date treatment of deep learning covers cutting-edge topics not found in existing texts, such as transformers and diffusion models Short, focused chapters progress in complexity, easing students into difficult concepts Pragmatic approach straddling theory and practice gives readers the level of detail required to implement naive versions of models Streamlined presentation separates critical ideas from background context and extraneous detail Minimal mathematical prerequisites, extensive illustrations, and practice problems make challenging material widely accessible Programming exercises offered in accompanying Python Notebooks

Interpretable Machine Learning

Christoph Molnar
Interpretable Machine Learning Book PDF

Author : Christoph Molnar
Publisher : Lulu.com
Page : 320 pages
File Size : 42,5 Mb
Release : 2020
Category : Artificial intelligence
ISBN : 9780244768522

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Interpretable Machine Learning by Christoph Molnar Pdf

This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Understanding Machine Learning

Shai Shalev-Shwartz,Shai Ben-David
Understanding Machine Learning Book PDF

Author : Shai Shalev-Shwartz,Shai Ben-David
Publisher : Cambridge University Press
Page : 415 pages
File Size : 50,8 Mb
Release : 2014-05-19
Category : Computers
ISBN : 9781107057135

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Understanding Machine Learning by Shai Shalev-Shwartz,Shai Ben-David Pdf

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Deep Learning for Coders with fastai and PyTorch

Jeremy Howard,Sylvain Gugger
Deep Learning for Coders with fastai and PyTorch Book PDF

Author : Jeremy Howard,Sylvain Gugger
Publisher : O'Reilly Media
Page : 624 pages
File Size : 42,8 Mb
Release : 2020-06-29
Category : Computers
ISBN : 9781492045496

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Deep Learning for Coders with fastai and PyTorch by Jeremy Howard,Sylvain Gugger Pdf

Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala

The Deep Learning Revolution

Terrence J. Sejnowski
The Deep Learning Revolution Book PDF

Author : Terrence J. Sejnowski
Publisher : MIT Press
Page : 354 pages
File Size : 40,5 Mb
Release : 2018-10-23
Category : Computers
ISBN : 9780262038034

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The Deep Learning Revolution by Terrence J. Sejnowski Pdf

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.