Convolutional Neural Networks

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Guide to Convolutional Neural Networks

Author : Hamed Habibi Aghdam
Publisher : Springer
ISBN 13 : 3319575503
Total Pages : 303 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis Guide to Convolutional Neural Networks by : Hamed Habibi Aghdam

Download or read book Guide to Convolutional Neural Networks written by Hamed Habibi Aghdam and published by Springer. This book was released on 2017-05-17 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: This must-read text/reference introduces the fundamental concepts of convolutional neural networks (ConvNets), offering practical guidance on using libraries to implement ConvNets in applications of traffic sign detection and classification. The work presents techniques for optimizing the computational efficiency of ConvNets, as well as visualization techniques to better understand the underlying processes. The proposed models are also thoroughly evaluated from different perspectives, using exploratory and quantitative analysis. Topics and features: explains the fundamental concepts behind training linear classifiers and feature learning; discusses the wide range of loss functions for training binary and multi-class classifiers; illustrates how to derive ConvNets from fully connected neural networks, and reviews different techniques for evaluating neural networks; presents a practical library for implementing ConvNets, explaining how to use a Python interface for the library to create and assess neural networks; describes two real-world examples of the detection and classification of traffic signs using deep learning methods; examines a range of varied techniques for visualizing neural networks, using a Python interface; provides self-study exercises at the end of each chapter, in addition to a helpful glossary, with relevant Python scripts supplied at an associated website. This self-contained guide will benefit those who seek to both understand the theory behind deep learning, and to gain hands-on experience in implementing ConvNets in practice. As no prior background knowledge in the field is required to follow the material, the book is ideal for all students of computer vision and machine learning, and will also be of great interest to practitioners working on autonomous cars and advanced driver assistance systems.

Learning TensorFlow

Author : Tom Hope
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491978481
Total Pages : 242 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Learning TensorFlow by : Tom Hope

Download or read book Learning TensorFlow written by Tom Hope and published by "O'Reilly Media, Inc.". This book was released on 2017-08-09 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: Roughly inspired by the human brain, deep neural networks trained with large amounts of data can solve complex tasks with unprecedented accuracy. This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for computer vision, natural language processing (NLP), speech recognition, and general predictive analytics. Authors Tom Hope, Yehezkel Resheff, and Itay Lieder provide a hands-on approach to TensorFlow fundamentals for a broad technical audience—from data scientists and engineers to students and researchers. You’ll begin by working through some basic examples in TensorFlow before diving deeper into topics such as neural network architectures, TensorBoard visualization, TensorFlow abstraction libraries, and multithreaded input pipelines. Once you finish this book, you’ll know how to build and deploy production-ready deep learning systems in TensorFlow. Get up and running with TensorFlow, rapidly and painlessly Learn how to use TensorFlow to build deep learning models from the ground up Train popular deep learning models for computer vision and NLP Use extensive abstraction libraries to make development easier and faster Learn how to scale TensorFlow, and use clusters to distribute model training Deploy TensorFlow in a production setting

Convolutional Neural Networks in Visual Computing

Author : Ragav Venkatesan
Publisher : CRC Press
ISBN 13 : 1351650327
Total Pages : 204 pages
Book Rating : 4.3/5 (516 download)

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Book Synopsis Convolutional Neural Networks in Visual Computing by : Ragav Venkatesan

Download or read book Convolutional Neural Networks in Visual Computing written by Ragav Venkatesan and published by CRC Press. This book was released on 2017-10-23 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the fundamentals in designing and deploying techniques using deep architectures. It is intended to serve as a beginner's guide to engineers or students who want to have a quick start on learning and/or building deep learning systems. This book provides a good theoretical and practical understanding and a complete toolkit of basic information and knowledge required to understand and build convolutional neural networks (CNN) from scratch. The book focuses explicitly on convolutional neural networks, filtering out other material that co-occur in many deep learning books on CNN topics.

Practical Convolutional Neural Networks

Author : Mohit Sewak
Publisher : Packt Publishing Ltd
ISBN 13 : 1788394143
Total Pages : 211 pages
Book Rating : 4.7/5 (883 download)

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Book Synopsis Practical Convolutional Neural Networks by : Mohit Sewak

Download or read book Practical Convolutional Neural Networks written by Mohit Sewak and published by Packt Publishing Ltd. This book was released on 2018-02-27 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: One stop guide to implementing award-winning, and cutting-edge CNN architectures Key Features Fast-paced guide with use cases and real-world examples to get well versed with CNN techniques Implement CNN models on image classification, transfer learning, Object Detection, Instance Segmentation, GANs and more Implement powerful use-cases like image captioning, reinforcement learning for hard attention, and recurrent attention models Book Description Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision related problems and implement real-life CNN models. This book starts with an overview of deep neural networkswith the example of image classification and walks you through building your first CNN for human face detector. We will learn to use concepts like transfer learning with CNN, and Auto-Encoders to build very powerful models, even when not much of supervised training data of labeled images is available. Later we build upon the learning achieved to build advanced vision related algorithms for object detection, instance segmentation, generative adversarial networks, image captioning, attention mechanisms for vision, and recurrent models for vision. By the end of this book, you should be ready to implement advanced, effective and efficient CNN models at your professional project or personal initiatives by working on complex image and video datasets. What you will learn From CNN basic building blocks to advanced concepts understand practical areas they can be applied to Build an image classifier CNN model to understand how different components interact with each other, and then learn how to optimize it Learn different algorithms that can be applied to Object Detection, and Instance Segmentation Learn advanced concepts like attention mechanisms for CNN to improve prediction accuracy Understand transfer learning and implement award-winning CNN architectures like AlexNet, VGG, GoogLeNet, ResNet and more Understand the working of generative adversarial networks and how it can create new, unseen images Who this book is for This book is for data scientists, machine learning and deep learning practitioners, Cognitive and Artificial Intelligence enthusiasts who want to move one step further in building Convolutional Neural Networks. Get hands-on experience with extreme datasets and different CNN architectures to build efficient and smart ConvNet models. Basic knowledge of deep learning concepts and Python programming language is expected.

Multivariate Statistical Machine Learning Methods for Genomic Prediction

Author : Osval Antonio Montesinos López
Publisher : Springer Nature
ISBN 13 : 3030890104
Total Pages : 707 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Multivariate Statistical Machine Learning Methods for Genomic Prediction by : Osval Antonio Montesinos López

Download or read book Multivariate Statistical Machine Learning Methods for Genomic Prediction written by Osval Antonio Montesinos López and published by Springer Nature. This book was released on 2022-02-14 with total page 707 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.

Grokking Machine Learning

Author : Luis Serrano
Publisher : Simon and Schuster
ISBN 13 : 1617295914
Total Pages : 510 pages
Book Rating : 4.6/5 (172 download)

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Book Synopsis Grokking Machine Learning by : Luis Serrano

Download or read book Grokking Machine Learning written by Luis Serrano and published by Simon and Schuster. This book was released on 2021-12-14 with total page 510 pages. Available in PDF, EPUB and Kindle. Book excerpt: Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you'll build interesting projects with Python, including models for spam detection and image recognition. You'll also pick up practical skills for cleaning and preparing data.

Rough Sets and Knowledge Technology

Author : Duoqian Miao
Publisher : Springer
ISBN 13 : 3319117408
Total Pages : 877 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Rough Sets and Knowledge Technology by : Duoqian Miao

Download or read book Rough Sets and Knowledge Technology written by Duoqian Miao and published by Springer. This book was released on 2014-09-25 with total page 877 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed conference proceedings of the 9th International Conference on Rough Sets and Knowledge Technology, RSKT 2014, held in Shanghai, China, in October 2014. The 70 papers presented were carefully reviewed and selected from 162 submissions. The papers in this volume cover topics such as foundations and generalizations of rough sets, attribute reduction and feature selection, applications of rough sets, intelligent systems and applications, knowledge technology, domain-oriented data-driven data mining, uncertainty in granular computing, advances in granular computing, big data to wise decisions, rough set theory, and three-way decisions, uncertainty, and granular computing.

A Guide to Convolutional Neural Networks for Computer Vision

Author : Salman Khan
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1681732823
Total Pages : 284 pages
Book Rating : 4.6/5 (817 download)

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Book Synopsis A Guide to Convolutional Neural Networks for Computer Vision by : Salman Khan

Download or read book A Guide to Convolutional Neural Networks for Computer Vision written by Salman Khan and published by Morgan & Claypool Publishers. This book was released on 2018-02-13 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks of many of these applications, and recent developments in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems. As a result, CNNs now form the crux of deep learning algorithms in computer vision. This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision. It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks: training, regularization, and optimization of CNNs. The book also discusses a wide range of loss functions, network layers, and popular CNN architectures, reviews the different techniques for the evaluation of CNNs, and presents some popular CNN tools and libraries that are commonly used in computer vision. Further, this text describes and discusses case studies that are related to the application of CNN in computer vision, including image classification, object detection, semantic segmentation, scene understanding, and image generation. This book is ideal for undergraduate and graduate students, as no prior background knowledge in the field is required to follow the material, as well as new researchers, developers, engineers, and practitioners who are interested in gaining a quick understanding of CNN models.

Hands-On Convolutional Neural Networks with TensorFlow

Author : Iffat Zafar
Publisher : Packt Publishing Ltd
ISBN 13 : 1789132827
Total Pages : 264 pages
Book Rating : 4.7/5 (891 download)

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Book Synopsis Hands-On Convolutional Neural Networks with TensorFlow by : Iffat Zafar

Download or read book Hands-On Convolutional Neural Networks with TensorFlow written by Iffat Zafar and published by Packt Publishing Ltd. This book was released on 2018-08-28 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems with this practical guide on training CNNs for image classification, image recognition, object detection and many computer vision challenges. Key Features Learn the fundamentals of Convolutional Neural Networks Harness Python and Tensorflow to train CNNs Build scalable deep learning models that can process millions of items Book Description Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time! We start with an overview of popular machine learning and deep learning models, and then get you set up with a TensorFlow development environment. This environment is the basis for implementing and training deep learning models in later chapters. Then, you will use Convolutional Neural Networks to work on problems such as image classification, object detection, and semantic segmentation. After that, you will use transfer learning to see how these models can solve other deep learning problems. You will also get a taste of implementing generative models such as autoencoders and generative adversarial networks. Later on, you will see useful tips on machine learning best practices and troubleshooting. Finally, you will learn how to apply your models on large datasets of millions of images. What you will learn Train machine learning models with TensorFlow Create systems that can evolve and scale during their life cycle Use CNNs in image recognition and classification Use TensorFlow for building deep learning models Train popular deep learning models Fine-tune a neural network to improve the quality of results with transfer learning Build TensorFlow models that can scale to large datasets and systems Who this book is for This book is for Software Engineers, Data Scientists, or Machine Learning practitioners who want to use CNNs for solving real-world problems. Knowledge of basic machine learning concepts, linear algebra and Python will help.

Interpretable Machine Learning

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

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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.

Deep Learning Applications in Medical Imaging

Author : Saxena, Sanjay
Publisher : IGI Global
ISBN 13 : 1799850722
Total Pages : 274 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Deep Learning Applications in Medical Imaging by : Saxena, Sanjay

Download or read book Deep Learning Applications in Medical Imaging written by Saxena, Sanjay and published by IGI Global. This book was released on 2020-10-16 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: Before the modern age of medicine, the chance of surviving a terminal disease such as cancer was minimal at best. After embracing the age of computer-aided medical analysis technologies, however, detecting and preventing individuals from contracting a variety of life-threatening diseases has led to a greater survival percentage and increased the development of algorithmic technologies in healthcare. Deep Learning Applications in Medical Imaging is a pivotal reference source that provides vital research on the application of generating pictorial depictions of the interior of a body for medical intervention and clinical analysis. While highlighting topics such as artificial neural networks, disease prediction, and healthcare analysis, this publication explores image acquisition and pattern recognition as well as the methods of treatment and care. This book is ideally designed for diagnosticians, medical imaging specialists, healthcare professionals, physicians, medical researchers, academicians, and students.

Advanced Applied Deep Learning

Author : Umberto Michelucci
Publisher : Apress
ISBN 13 : 1484249763
Total Pages : 294 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Advanced Applied Deep Learning by : Umberto Michelucci

Download or read book Advanced Applied Deep Learning written by Umberto Michelucci and published by Apress. This book was released on 2019-09-28 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Develop and optimize deep learning models with advanced architectures. This book teaches you the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks. In Advanced Applied Deep Learning, you will study advanced topics on CNN and object detection using Keras and TensorFlow. Along the way, you will look at the fundamental operations in CNN, such as convolution and pooling, and then look at more advanced architectures such as inception networks, resnets, and many more. While the book discusses theoretical topics, you will discover how to work efficiently with Keras with many tricks and tips, including how to customize logging in Keras with custom callback classes, what is eager execution, and how to use it in your models. Finally, you will study how object detection works, and build a complete implementation of the YOLO (you only look once) algorithm in Keras and TensorFlow. By the end of the book you will have implemented various models in Keras and learned many advanced tricks that will bring your skills to the next level. What You Will Learn See how convolutional neural networks and object detection workSave weights and models on diskPause training and restart it at a later stage Use hardware acceleration (GPUs) in your codeWork with the Dataset TensorFlow abstraction and use pre-trained models and transfer learningRemove and add layers to pre-trained networks to adapt them to your specific projectApply pre-trained models such as Alexnet and VGG16 to new datasets Who This Book Is For Scientists and researchers with intermediate-to-advanced Python and machine learning know-how. Additionally, intermediate knowledge of Keras and TensorFlow is expected.

Convolutional Neural Networks In Python

Author : Frank Millstein
Publisher : Frank Millstein
ISBN 13 :
Total Pages : 119 pages
Book Rating : 4./5 ( download)

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Book Synopsis Convolutional Neural Networks In Python by : Frank Millstein

Download or read book Convolutional Neural Networks In Python written by Frank Millstein and published by Frank Millstein. This book was released on 2020-07-06 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: Convolutional Neural Networks in Python This book covers the basics behind Convolutional Neural Networks by introducing you to this complex world of deep learning and artificial neural networks in a simple and easy to understand way. It is perfect for any beginner out there looking forward to learning more about this machine learning field. This book is all about how to use convolutional neural networks for various image, object and other common classification problems in Python. Here, we also take a deeper look into various Keras layer used for building CNNs we take a look at different activation functions and much more, which will eventually lead you to creating highly accurate models able of performing great task results on various image classification, object classification and other problems. Therefore, at the end of the book, you will have a better insight into this world, thus you will be more than prepared to deal with more complex and challenging tasks on your own. Here Is a Preview of What You’ll Learn In This Book… Convolutional neural networks structure How convolutional neural networks actually work Convolutional neural networks applications The importance of convolution operator Different convolutional neural networks layers and their importance Arrangement of spatial parameters How and when to use stride and zero-padding Method of parameter sharing Matrix multiplication and its importance Pooling and dense layers Introducing non-linearity relu activation function How to train your convolutional neural network models using backpropagation How and why to apply dropout CNN model training process How to build a convolutional neural network Generating predictions and calculating loss functions How to train and evaluate your MNIST classifier How to build a simple image classification CNN And much, much more! Get this book NOW and learn more about Convolutional Neural Networks in Python!

Convolutional Neural Networks with Swift for Tensorflow

Author : Brett Koonce
Publisher :
ISBN 13 : 9781484261699
Total Pages : 0 pages
Book Rating : 4.2/5 (616 download)

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Book Synopsis Convolutional Neural Networks with Swift for Tensorflow by : Brett Koonce

Download or read book Convolutional Neural Networks with Swift for Tensorflow written by Brett Koonce and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dive into and apply practical machine learning and dataset categorization techniques while learning Tensorflow and deep learning. This book uses convolutional neural networks to do image recognition all in the familiar and easy to work with Swift language. It begins with a basic machine learning overview and then ramps up to neural networks and convolutions and how they work. Using Swift and Tensorflow, you'll perform data augmentation, build and train large networks, and build networks for mobile devices. You'll also cover cloud training and the network you build can categorize greyscale data, such as mnist, to large scale modern approaches that can categorize large datasets, such as imagenet. Convolutional Neural Networks with Swift for Tensorflow uses a simple approach that adds progressive layers of complexity until you have arrived at the current state of the art for this field. You will: Categorize and augment datasets Build and train large networks, including via cloud solutions Deploy complex systems to mobile devices.

Deep Learning and Convolutional Neural Networks for Medical Image Computing

Author : Le Lu
Publisher : Springer
ISBN 13 : 331942999X
Total Pages : 327 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis Deep Learning and Convolutional Neural Networks for Medical Image Computing by : Le Lu

Download or read book Deep Learning and Convolutional Neural Networks for Medical Image Computing written by Le Lu and published by Springer. This book was released on 2017-07-12 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. Features: highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in medical image computing; discusses the insightful research experience of Dr. Ronald M. Summers; presents a comprehensive review of the latest research and literature; describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging; examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database.

Convolutional Neural Networks for Medical Applications

Author : Teik Toe Teoh
Publisher : Springer Nature
ISBN 13 : 9811988145
Total Pages : 103 pages
Book Rating : 4.8/5 (119 download)

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Book Synopsis Convolutional Neural Networks for Medical Applications by : Teik Toe Teoh

Download or read book Convolutional Neural Networks for Medical Applications written by Teik Toe Teoh and published by Springer Nature. This book was released on 2023-03-23 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: Convolutional Neural Networks for Medical Applications consists of research investigated by the author, containing state-of-the-art knowledge, authored by Dr Teoh Teik Toe, in applying Convolutional Neural Networks (CNNs) to the medical imagery domain. This book will expose researchers to various applications and techniques applied with deep learning on medical images, as well as unique techniques to enhance the performance of these networks.Through the various chapters and topics covered, this book provides knowledge about the fundamentals of deep learning to a common reader while allowing a research scholar to identify some futuristic problem areas. The topics covered include brain tumor classification, pneumonia image classification, white blood cell classification, skin cancer classification and diabetic retinopathy detection. The first chapter will begin by introducing various topics used in training CNNs to help readers with common concepts covered across the book. Each chapter begins by providing information about the disease, its implications to the affected and how the use of CNNs can help to tackle issues faced in healthcare. Readers would be exposed to various performance enhancement techniques, which have been tried and tested successfully, such as specific data augmentations and image processing techniques utilized to improve the accuracy of the models.

Artificial Neural Networks and Machine Learning -- ICANN 2014

Author : Stefan Wermter
Publisher : Springer
ISBN 13 : 3319111795
Total Pages : 874 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Artificial Neural Networks and Machine Learning -- ICANN 2014 by : Stefan Wermter

Download or read book Artificial Neural Networks and Machine Learning -- ICANN 2014 written by Stefan Wermter and published by Springer. This book was released on 2014-08-18 with total page 874 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book constitutes the proceedings of the 24th International Conference on Artificial Neural Networks, ICANN 2014, held in Hamburg, Germany, in September 2014. The 107 papers included in the proceedings were carefully reviewed and selected from 173 submissions. The focus of the papers is on following topics: recurrent networks; competitive learning and self-organisation; clustering and classification; trees and graphs; human-machine interaction; deep networks; theory; reinforcement learning and action; vision; supervised learning; dynamical models and time series; neuroscience; and applications.