Learning Deep Visual Representations

Download Learning Deep Visual Representations full books in PDF, epub, and Kindle. Read online Learning Deep Visual Representations ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Learning Deep Visual Representations

Author : Hanlin Goh
Publisher :
ISBN 13 :
Total Pages : 167 pages
Book Rating : 4.:/5 (122 download)

DOWNLOAD NOW!

Book Synopsis Learning Deep Visual Representations by : Hanlin Goh

Download or read book Learning Deep Visual Representations written by Hanlin Goh and published by . This book was released on 2013 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advancements in the areas of deep learning and visual information processing have presented an opportunity to unite both fields. These complementary fields combine to tackle the problem of classifying images into their semantic categories. Deep learning brings learning and representational capabilities to a visual processing model that is adapted for image classification. This thesis addresses problems that lead to the proposal of learning deep visual representations for image classification.The problem of deep learning is tackled on two fronts. The first aspect is the problem of unsupervised learning of latent representations from input data. The main focus is the integration of prior knowledge into the learning of restricted Boltzmann machines (RBM) through regularization. Regularizers are proposed to induce sparsity, selectivity and topographic organization in the coding to improve discrimination and invariance. The second direction introduces the notion of gradually transiting from unsupervised layer-wise learning to supervised deep learning. This is done through the integration of bottom-up information with top-down signals. Two novel implementations supporting this notion are explored. The first method uses top-down regularization to train a deep network of RBMs. The second method combines predictive and reconstructive loss functions to optimize a stack of encoder-decoder networks.The proposed deep learning techniques are applied to tackle the image classification problem. The bag-of-words model is adopted due to its strengths in image modeling through the use of local image descriptors and spatial pooling schemes. Deep learning with spatial aggregation is used to learn a hierarchical visual dictionary for encoding the image descriptors into mid-level representations. This method achieves leading image classification performances for object and scene images. The learned dictionaries are diverse and non-redundant. The speed of inference is also high. From this, a further optimization is performed for the subsequent pooling step. This is done by introducing a differentiable pooling parameterization and applying the error backpropagation algorithm.This thesis represents one of the first attempts to synthesize deep learning and the bag-of-words model. This union results in many challenging research problems, leaving much room for further study in this area.

Exploring Visual Structures in Deep Representation Learning

Author : Weijian Xu
Publisher :
ISBN 13 :
Total Pages : 127 pages
Book Rating : 4.:/5 (13 download)

DOWNLOAD NOW!

Book Synopsis Exploring Visual Structures in Deep Representation Learning by : Weijian Xu

Download or read book Exploring Visual Structures in Deep Representation Learning written by Weijian Xu and published by . This book was released on 2022 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep representation learning has dominated almost every task in computer vision and achieves superior performance. In deep representation learning, deep neural networks are trained on massive data to provide rich visual representations. However, general-purpose neural networks are not fully aware of visual structures, which limit their generalizability on specific vision tasks (e.g., skeleton detection and line segment detection) or under particular scenarios (e.g., few-shot settings). To tackle the aforementioned limitation, we find it natural and essential to enhance the deep representation with visual structures. This dissertation concentrates on three visual structures: geometric structure, part structure, and multi-scale structure. We then present four examples to study these visual structures in deep representation learning. First, we focus on object skeleton detection and introduce geometric structure in objective function design. Second, we encode part structure in a convolutional neural network for the few-shot image classification. Third, we build a generic vision Transformer with a co-scale structure for image recognition and instance-level prediction. Finally, we present a Transformer model with a multi-scale structure in line segment detection.

Deep Learning

Author : Andrew Glassner
Publisher : No Starch Press
ISBN 13 : 1718500734
Total Pages : 1315 pages
Book Rating : 4.7/5 (185 download)

DOWNLOAD NOW!

Book Synopsis Deep Learning by : Andrew Glassner

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 1315 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

Deep Learning Illustrated

Author : Jon Krohn
Publisher : Addison-Wesley Professional
ISBN 13 : 0135121728
Total Pages : 725 pages
Book Rating : 4.1/5 (351 download)

DOWNLOAD NOW!

Book Synopsis Deep Learning Illustrated by : Jon Krohn

Download or read book Deep Learning Illustrated written by Jon Krohn and published by Addison-Wesley Professional. This book was released on 2019-08-05 with total page 725 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come." – Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn. World-class instructor and practitioner Jon Krohn–with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens–presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered. You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms. Discover what makes deep learning systems unique, and the implications for practitioners Explore new tools that make deep learning models easier to build, use, and improve Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Deep Learning

Author : Ian Goodfellow
Publisher : MIT Press
ISBN 13 : 0262337371
Total Pages : 801 pages
Book Rating : 4.2/5 (623 download)

DOWNLOAD NOW!

Book Synopsis Deep Learning by : Ian Goodfellow

Download or read book Deep Learning written by Ian Goodfellow and published by MIT Press. This book was released on 2016-11-10 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.

Graph Representation Learning

Author : William L. William L. Hamilton
Publisher : Springer Nature
ISBN 13 : 3031015886
Total Pages : 141 pages
Book Rating : 4.0/5 (31 download)

DOWNLOAD NOW!

Book Synopsis Graph Representation Learning by : William L. William L. Hamilton

Download or read book Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Neuroscience-driven Visual Representation

Author : Teng Li
Publisher : Frontiers Media SA
ISBN 13 : 2832553222
Total Pages : 134 pages
Book Rating : 4.8/5 (325 download)

DOWNLOAD NOW!

Book Synopsis Neuroscience-driven Visual Representation by : Teng Li

Download or read book Neuroscience-driven Visual Representation written by Teng Li and published by Frontiers Media SA. This book was released on 2024-08-14 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: Visual representation learning enables computers or systems to simulate the function of retinas, optic nerves, and visual cortex in the human brain, and derive meaningful information from digital images, videos, and other visual inputs. To learn the effective presentation of visual data is essential for many computer vision and artificial intelligence applications ranging from energy and utilities to manufacturing and automotive. Current popular deep learning-based visual representation learning methods do not fully consider the nature of the biological visual nervous system and are lack in interpretability. To solve visual representation well, the integration of psychological or neuroscientific approaches is required to enhance the cognition of visual data.

Deep Learning for Robot Perception and Cognition

Author : Alexandros Iosifidis
Publisher : Academic Press
ISBN 13 : 0323885721
Total Pages : 638 pages
Book Rating : 4.3/5 (238 download)

DOWNLOAD NOW!

Book Synopsis Deep Learning for Robot Perception and Cognition by : Alexandros Iosifidis

Download or read book Deep Learning for Robot Perception and Cognition written by Alexandros Iosifidis and published by Academic Press. This book was released on 2022-02-04 with total page 638 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. - Presents deep learning principles and methodologies - Explains the principles of applying end-to-end learning in robotics applications - Presents how to design and train deep learning models - Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more - Uses robotic simulation environments for training deep learning models - Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis

Advanced Methods and Deep Learning in Computer Vision

Author : E. R. Davies
Publisher : Academic Press
ISBN 13 : 0128221496
Total Pages : 584 pages
Book Rating : 4.1/5 (282 download)

DOWNLOAD NOW!

Book Synopsis Advanced Methods and Deep Learning in Computer Vision by : E. R. Davies

Download or read book Advanced Methods and Deep Learning in Computer Vision written by E. R. Davies and published by Academic Press. This book was released on 2021-11-09 with total page 584 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced Methods and Deep Learning in Computer Vision presents advanced computer vision methods, emphasizing machine and deep learning techniques that have emerged during the past 5–10 years. The book provides clear explanations of principles and algorithms supported with applications. Topics covered include machine learning, deep learning networks, generative adversarial networks, deep reinforcement learning, self-supervised learning, extraction of robust features, object detection, semantic segmentation, linguistic descriptions of images, visual search, visual tracking, 3D shape retrieval, image inpainting, novelty and anomaly detection. This book provides easy learning for researchers and practitioners of advanced computer vision methods, but it is also suitable as a textbook for a second course on computer vision and deep learning for advanced undergraduates and graduate students. - Provides an important reference on deep learning and advanced computer methods that was created by leaders in the field - Illustrates principles with modern, real-world applications - Suitable for self-learning or as a text for graduate courses

Introduction to Deep Learning

Author : Sandro Skansi
Publisher : Springer
ISBN 13 : 3319730045
Total Pages : 196 pages
Book Rating : 4.3/5 (197 download)

DOWNLOAD NOW!

Book Synopsis Introduction to Deep Learning by : Sandro Skansi

Download or read book Introduction to Deep Learning written by Sandro Skansi and published by Springer. This book was released on 2018-02-04 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website. Topics and features: introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks, and the recurrent connections to a feed-forward neural network; describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning; presents a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism. This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.

Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

Author : Wojciech Samek
Publisher : Springer Nature
ISBN 13 : 3030289540
Total Pages : 435 pages
Book Rating : 4.0/5 (32 download)

DOWNLOAD NOW!

Book Synopsis Explainable AI: Interpreting, Explaining and Visualizing Deep Learning by : Wojciech Samek

Download or read book Explainable AI: Interpreting, Explaining and Visualizing Deep Learning written by Wojciech Samek and published by Springer Nature. This book was released on 2019-09-10 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.

Visual Representation Learning with Deep Neural Networks Under Label and Budget Constraints

Author : Eric Arazo Sánchez
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (141 download)

DOWNLOAD NOW!

Book Synopsis Visual Representation Learning with Deep Neural Networks Under Label and Budget Constraints by : Eric Arazo Sánchez

Download or read book Visual Representation Learning with Deep Neural Networks Under Label and Budget Constraints written by Eric Arazo Sánchez and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents the work done in the area of semi-supervised learning, label noise, and budgeted training for deep learning approaches to computer vision. The improvements seen in computer vision since the successful introduction of deep learning rely on the availability of large amounts of labeled data and long lasting training processes. First, this research studies the three main alternatives to fully supervised deep learning categorized in three different levels of supervision: unsupervised learning (no label involved), semi-supervised learning (a small set of labeled data is available), and label noise (all the samples are labeled but some of them are incorrect). These alternatives aim at reducing the cost of building fully annotated and finely curated datasets, which in most cases is time consuming and requires expert annotators. State-of-the-art performance has been achieved in several semi-supervised, unsupervised, and label noise benchmarks including CIFAR10, CIFAR100, and STL-10. Additionally, the solutions proposed for learning in the presence of label noise have been validated in realistic benchmarks built with datasets annotated from web information: WebVision and Clothing1M. Second, this research explores alternatives to reduce the computational cost of the training of deep learning systems that currently require hours or days to reach state-of-the-art performance. Particularly, this research studied budgeted training, i.e.~when the training process is limited to a fixed number of iterations. Experiments in this setup showed that for better model convergence, variety in the data is preferable than the importance of the samples used during training. As a result of this research, three main author publications have been generated, one more has been recently submitted to review for a conference, and several other secondary author publications have been produced in close collaboration with other researchers in the centre.

ECAI 2016

Author : G.A. Kaminka
Publisher : IOS Press
ISBN 13 : 1614996725
Total Pages : 1860 pages
Book Rating : 4.6/5 (149 download)

DOWNLOAD NOW!

Book Synopsis ECAI 2016 by : G.A. Kaminka

Download or read book ECAI 2016 written by G.A. Kaminka and published by IOS Press. This book was released on 2016-08-24 with total page 1860 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence continues to be one of the most exciting and fast-developing fields of computer science. This book presents the 177 long papers and 123 short papers accepted for ECAI 2016, the latest edition of the biennial European Conference on Artificial Intelligence, Europe’s premier venue for presenting scientific results in AI. The conference was held in The Hague, the Netherlands, from August 29 to September 2, 2016. ECAI 2016 also incorporated the conference on Prestigious Applications of Intelligent Systems (PAIS) 2016, and the Starting AI Researcher Symposium (STAIRS). The papers from PAIS are included in this volume; the papers from STAIRS are published in a separate volume in the Frontiers in Artificial Intelligence and Applications (FAIA) series. Organized by the European Association for Artificial Intelligence (EurAI) and the Benelux Association for Artificial Intelligence (BNVKI), the ECAI conference provides an opportunity for researchers to present and hear about the very best research in contemporary AI. This proceedings will be of interest to all those seeking an overview of the very latest innovations and developments in this field.

Deep Learning for Image Processing Applications

Author : D.J. Hemanth
Publisher : IOS Press
ISBN 13 : 1614998221
Total Pages : 284 pages
Book Rating : 4.6/5 (149 download)

DOWNLOAD NOW!

Book Synopsis Deep Learning for Image Processing Applications by : D.J. Hemanth

Download or read book Deep Learning for Image Processing Applications written by D.J. Hemanth and published by IOS Press. This book was released on 2017-12 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning and image processing are two areas of great interest to academics and industry professionals alike. The areas of application of these two disciplines range widely, encompassing fields such as medicine, robotics, and security and surveillance. The aim of this book, ‘Deep Learning for Image Processing Applications’, is to offer concepts from these two areas in the same platform, and the book brings together the shared ideas of professionals from academia and research about problems and solutions relating to the multifaceted aspects of the two disciplines. The first chapter provides an introduction to deep learning, and serves as the basis for much of what follows in the subsequent chapters, which cover subjects including: the application of deep neural networks for image classification; hand gesture recognition in robotics; deep learning techniques for image retrieval; disease detection using deep learning techniques; and the comparative analysis of deep data and big data. The book will be of interest to all those whose work involves the use of deep learning and image processing techniques.

Pattern Recognition

Author : Christian Bauckhage
Publisher : Springer Nature
ISBN 13 : 3030926591
Total Pages : 734 pages
Book Rating : 4.0/5 (39 download)

DOWNLOAD NOW!

Book Synopsis Pattern Recognition by : Christian Bauckhage

Download or read book Pattern Recognition written by Christian Bauckhage and published by Springer Nature. This book was released on 2022-01-13 with total page 734 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 43rd DAGM German Conference on Pattern Recognition, DAGM GCPR 2021, which was held during September 28 – October 1, 2021. The conference was planned to take place in Bonn, Germany, but changed to a virtual event due to the COVID-19 pandemic. The 46 papers presented in this volume were carefully reviewed and selected from 116 submissions. They were organized in topical sections as follows: machine learning and optimization; actions, events, and segmentation; generative models and multimodal data; labeling and self-supervised learning; applications; and 3D modelling and reconstruction.

Computational Texture and Patterns

Author : Kristin J. Dana
Publisher : Morgan & Claypool Publishers
ISBN 13 : 168173012X
Total Pages : 115 pages
Book Rating : 4.6/5 (817 download)

DOWNLOAD NOW!

Book Synopsis Computational Texture and Patterns by : Kristin J. Dana

Download or read book Computational Texture and Patterns written by Kristin J. Dana and published by Morgan & Claypool Publishers. This book was released on 2018-09-13 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: Visual pattern analysis is a fundamental tool in mining data for knowledge. Computational representations for patterns and texture allow us to summarize, store, compare, and label in order to learn about the physical world. Our ability to capture visual imagery with cameras and sensors has resulted in vast amounts of raw data, but using this information effectively in a task-specific manner requires sophisticated computational representations. We enumerate specific desirable traits for these representations: (1) intraclass invariance—to support recognition; (2) illumination and geometric invariance for robustness to imaging conditions; (3) support for prediction and synthesis to use the model to infer continuation of the pattern; (4) support for change detection to detect anomalies and perturbations; and (5) support for physics-based interpretation to infer system properties from appearance. In recent years, computer vision has undergone a metamorphosis with classic algorithms adapting to new trends in deep learning. This text provides a tour of algorithm evolution including pattern recognition, segmentation and synthesis. We consider the general relevance and prominence of visual pattern analysis and applications that rely on computational models.

Interpretable Machine Learning

Author : Christoph Molnar
Publisher : Lulu.com
ISBN 13 : 0244768528
Total Pages : 320 pages
Book Rating : 4.2/5 (447 download)

DOWNLOAD NOW!

Book Synopsis Interpretable Machine Learning by : Christoph Molnar

Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.