Deep Learning for Computer Vision with SAS

Deep Learning for Computer Vision with SAS

Author: Robert Blanchard

Publisher: SAS Institute

Published: 2020-06-12

Total Pages: 120

ISBN-13: 1642959170

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Discover deep learning and computer vision with SAS! Deep Learning for Computer Vision with SAS®: An Introduction introduces the pivotal components of deep learning. Readers will gain an in-depth understanding of how to build deep feedforward and convolutional neural networks, as well as variants of denoising autoencoders. Transfer learning is covered to help readers learn about this emerging field. Containing a mix of theory and application, this book will also briefly cover methods for customizing deep learning models to solve novel business problems or answer research questions. SAS programs and data are included to reinforce key concepts and allow readers to follow along with included demonstrations. Readers will learn how to: Define and understand deep learning Build models using deep learning techniques and SAS Viya Apply models to score (inference) new data Modify data for better analysis results Search the hyperparameter space of a deep learning model Leverage transfer learning using supervised and unsupervised methods


Book Synopsis Deep Learning for Computer Vision with SAS by : Robert Blanchard

Download or read book Deep Learning for Computer Vision with SAS written by Robert Blanchard and published by SAS Institute. This book was released on 2020-06-12 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover deep learning and computer vision with SAS! Deep Learning for Computer Vision with SAS®: An Introduction introduces the pivotal components of deep learning. Readers will gain an in-depth understanding of how to build deep feedforward and convolutional neural networks, as well as variants of denoising autoencoders. Transfer learning is covered to help readers learn about this emerging field. Containing a mix of theory and application, this book will also briefly cover methods for customizing deep learning models to solve novel business problems or answer research questions. SAS programs and data are included to reinforce key concepts and allow readers to follow along with included demonstrations. Readers will learn how to: Define and understand deep learning Build models using deep learning techniques and SAS Viya Apply models to score (inference) new data Modify data for better analysis results Search the hyperparameter space of a deep learning model Leverage transfer learning using supervised and unsupervised methods

Computer Vision with SAS

Computer Vision with SAS

Author:

Publisher:

Published: 2022

Total Pages:

ISBN-13: 9781952365010

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Book Synopsis Computer Vision with SAS by :

Download or read book Computer Vision with SAS written by and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Computer Vision with SAS

Computer Vision with SAS

Author: Susan Kahler

Publisher:

Published: 2020-07-22

Total Pages: 112

ISBN-13: 9781952365041

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Computer vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. In recent years, computer vision has begun to rival and even surpass human visual abilities in many areas. SAS offers many different solutions to train computers to "see" by identifying and classifying objects, and several groundbreaking papers have been written to demonstrate these techniques. The papers included in this special collection demonstrate how the latest computer vision tools and techniques can be used to solve a variety of business problems.


Book Synopsis Computer Vision with SAS by : Susan Kahler

Download or read book Computer Vision with SAS written by Susan Kahler and published by . This book was released on 2020-07-22 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. In recent years, computer vision has begun to rival and even surpass human visual abilities in many areas. SAS offers many different solutions to train computers to "see" by identifying and classifying objects, and several groundbreaking papers have been written to demonstrate these techniques. The papers included in this special collection demonstrate how the latest computer vision tools and techniques can be used to solve a variety of business problems.

Deep Learning for Computer Vision with SAS

Deep Learning for Computer Vision with SAS

Author: Robert Blanchard

Publisher:

Published: 2020-06-12

Total Pages: 150

ISBN-13: 9781642959727

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Discover deep learning and computer vision with SAS! Deep Learning for Computer Vision with SAS(R) An Introduction introduces the pivotal components of deep learning. Readers will gain an in-depth understanding of how to build deep feedforward and convolutional neural networks, as well as variants of denoising autoencoders. Transfer learning is covered to help readers learn about this emerging field. Containing a mix of theory and application, this book will also briefly cover methods for customizing deep learning models to solve novel business problems or answer research questions. SAS programs and data are included to reinforce key concepts and allow readers to follow along with included demonstrations. Readers will learn how to: Define and understand deep learning Build models using deep learning techniques and SAS Viya Apply models to score (inference) new data Modify data for better analysis results Search the hyperparameter space of a deep learning model Leverage transfer learning using supervised and unsupervised methods


Book Synopsis Deep Learning for Computer Vision with SAS by : Robert Blanchard

Download or read book Deep Learning for Computer Vision with SAS written by Robert Blanchard and published by . This book was released on 2020-06-12 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover deep learning and computer vision with SAS! Deep Learning for Computer Vision with SAS(R) An Introduction introduces the pivotal components of deep learning. Readers will gain an in-depth understanding of how to build deep feedforward and convolutional neural networks, as well as variants of denoising autoencoders. Transfer learning is covered to help readers learn about this emerging field. Containing a mix of theory and application, this book will also briefly cover methods for customizing deep learning models to solve novel business problems or answer research questions. SAS programs and data are included to reinforce key concepts and allow readers to follow along with included demonstrations. Readers will learn how to: Define and understand deep learning Build models using deep learning techniques and SAS Viya Apply models to score (inference) new data Modify data for better analysis results Search the hyperparameter space of a deep learning model Leverage transfer learning using supervised and unsupervised methods

Machine Learning with SAS Viya

Machine Learning with SAS Viya

Author: SAS Institute Inc.

Publisher: SAS Institute

Published: 2020-05-29

Total Pages: 295

ISBN-13: 1951685377

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Master machine learning with SAS Viya! Machine learning can feel intimidating for new practitioners. Machine Learning with SAS Viya provides everything you need to know to get started with machine learning in SAS Viya, including decision trees, neural networks, and support vector machines. The analytics life cycle is covered from data preparation and discovery to deployment. Working with open-source code? Machine Learning with SAS Viya has you covered – step-by-step instructions are given on how to use SAS Model Manager tools with open source. SAS Model Studio features are highlighted to show how to carry out machine learning in SAS Viya. Demonstrations, practice tasks, and quizzes are included to help sharpen your skills. In this book, you will learn about: Supervised and unsupervised machine learning Data preparation and dealing with missing and unstructured data Model building and selection Improving and optimizing models Model deployment and monitoring performance


Book Synopsis Machine Learning with SAS Viya by : SAS Institute Inc.

Download or read book Machine Learning with SAS Viya written by SAS Institute Inc. and published by SAS Institute. This book was released on 2020-05-29 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master machine learning with SAS Viya! Machine learning can feel intimidating for new practitioners. Machine Learning with SAS Viya provides everything you need to know to get started with machine learning in SAS Viya, including decision trees, neural networks, and support vector machines. The analytics life cycle is covered from data preparation and discovery to deployment. Working with open-source code? Machine Learning with SAS Viya has you covered – step-by-step instructions are given on how to use SAS Model Manager tools with open source. SAS Model Studio features are highlighted to show how to carry out machine learning in SAS Viya. Demonstrations, practice tasks, and quizzes are included to help sharpen your skills. In this book, you will learn about: Supervised and unsupervised machine learning Data preparation and dealing with missing and unstructured data Model building and selection Improving and optimizing models Model deployment and monitoring performance

Natural Language Processing with SAS

Natural Language Processing with SAS

Author:

Publisher:

Published: 2020-08-31

Total Pages: 74

ISBN-13: 9781952363184

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Natural Language Processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret, and emulate written or spoken human language. NLP draws from many disciplines including human-generated linguistic rules, machine learning, and deep learning to fill the gap between human communication and machine understanding. The papers included in this special collection demonstrate how NLP can be used to scale the human act of reading, organizing, and quantifying text data.


Book Synopsis Natural Language Processing with SAS by :

Download or read book Natural Language Processing with SAS written by and published by . This book was released on 2020-08-31 with total page 74 pages. Available in PDF, EPUB and Kindle. Book excerpt: Natural Language Processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret, and emulate written or spoken human language. NLP draws from many disciplines including human-generated linguistic rules, machine learning, and deep learning to fill the gap between human communication and machine understanding. The papers included in this special collection demonstrate how NLP can be used to scale the human act of reading, organizing, and quantifying text data.

Computer Vision Using Deep Learning

Computer Vision Using Deep Learning

Author: Vaibhav Verdhan

Publisher: Apress

Published: 2021-02-15

Total Pages: 308

ISBN-13: 9781484266151

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Organizations spend huge resources in developing software that can perform the way a human does. Image classification, object detection and tracking, pose estimation, facial recognition, and sentiment estimation all play a major role in solving computer vision problems. This book will bring into focus these and other deep learning architectures and techniques to help you create solutions using Keras and the TensorFlow library. You'll also review mutliple neural network architectures, including LeNet, AlexNet, VGG, Inception, R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN, YOLO, and SqueezeNet and see how they work alongside Python code via best practices, tips, tricks, shortcuts, and pitfalls. All code snippets will be broken down and discussed thoroughly so you can implement the same principles in your respective environments. Computer Vision Using Deep Learning offers a comprehensive yet succinct guide that stitches DL and CV together to automate operations, reduce human intervention, increase capability, and cut the costs. What You'll Learn Examine deep learning code and concepts to apply guiding principals to your own projects Classify and evaluate various architectures to better understand your options in various use cases Go behind the scenes of basic deep learning functions to find out how they work Who This Book Is For Professional practitioners working in the fields of software engineering and data science. A working knowledge of Python is strongly recommended. Students and innovators working on advanced degrees in areas related to computer vision and Deep Learning.


Book Synopsis Computer Vision Using Deep Learning by : Vaibhav Verdhan

Download or read book Computer Vision Using Deep Learning written by Vaibhav Verdhan and published by Apress. This book was released on 2021-02-15 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Organizations spend huge resources in developing software that can perform the way a human does. Image classification, object detection and tracking, pose estimation, facial recognition, and sentiment estimation all play a major role in solving computer vision problems. This book will bring into focus these and other deep learning architectures and techniques to help you create solutions using Keras and the TensorFlow library. You'll also review mutliple neural network architectures, including LeNet, AlexNet, VGG, Inception, R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN, YOLO, and SqueezeNet and see how they work alongside Python code via best practices, tips, tricks, shortcuts, and pitfalls. All code snippets will be broken down and discussed thoroughly so you can implement the same principles in your respective environments. Computer Vision Using Deep Learning offers a comprehensive yet succinct guide that stitches DL and CV together to automate operations, reduce human intervention, increase capability, and cut the costs. What You'll Learn Examine deep learning code and concepts to apply guiding principals to your own projects Classify and evaluate various architectures to better understand your options in various use cases Go behind the scenes of basic deep learning functions to find out how they work Who This Book Is For Professional practitioners working in the fields of software engineering and data science. A working knowledge of Python is strongly recommended. Students and innovators working on advanced degrees in areas related to computer vision and Deep Learning.

Unstructured Data Analysis

Unstructured Data Analysis

Author: Matthew Windham

Publisher: SAS Institute

Published: 2018-09-14

Total Pages: 166

ISBN-13: 1635267099

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Unstructured data is the most voluminous form of data in the world, and several elements are critical for any advanced analytics practitioner leveraging SAS software to effectively address the challenge of deriving value from that data. This book covers the five critical elements of entity extraction, unstructured data, entity resolution, entity network mapping and analysis, and entity management. By following examples of how to apply processing to unstructured data, readers will derive tremendous long-term value from this book as they enhance the value they realize from SAS products.


Book Synopsis Unstructured Data Analysis by : Matthew Windham

Download or read book Unstructured Data Analysis written by Matthew Windham and published by SAS Institute. This book was released on 2018-09-14 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unstructured data is the most voluminous form of data in the world, and several elements are critical for any advanced analytics practitioner leveraging SAS software to effectively address the challenge of deriving value from that data. This book covers the five critical elements of entity extraction, unstructured data, entity resolution, entity network mapping and analysis, and entity management. By following examples of how to apply processing to unstructured data, readers will derive tremendous long-term value from this book as they enhance the value they realize from SAS products.

Deep Learning in Computer Vision

Deep Learning in Computer Vision

Author: Mahmoud Hassaballah

Publisher: CRC Press

Published: 2020-03-23

Total Pages: 261

ISBN-13: 1351003801

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


Book Synopsis Deep Learning in Computer Vision by : Mahmoud Hassaballah

Download or read book Deep Learning in Computer Vision written by Mahmoud Hassaballah and published by CRC Press. This book was released on 2020-03-23 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions to several image-related problems that had long remained unsolved or partially addressed. This book presents a collection of eleven chapters where each individual chapter explains the deep learning principles of a specific topic, introduces reviews of up-to-date techniques, and presents research findings to the computer vision community. The book covers a broad scope of topics in deep learning concepts and applications such as accelerating the convolutional neural network inference on field-programmable gate arrays, fire detection in surveillance applications, face recognition, action and activity recognition, semantic segmentation for autonomous driving, aerial imagery registration, robot vision, tumor detection, and skin lesion segmentation as well as skin melanoma classification. The content of this book has been organized such that each chapter can be read independently from the others. The book is a valuable companion for researchers, for postgraduate and possibly senior undergraduate students who are taking an advanced course in related topics, and for those who are interested in deep learning with applications in computer vision, image processing, and pattern recognition.

Deep Learning for Numerical Applications with SAS (Hardcover Edition)

Deep Learning for Numerical Applications with SAS (Hardcover Edition)

Author: Henry Bequet

Publisher:

Published: 2019-08-16

Total Pages: 234

ISBN-13: 9781642953565

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Foreword by Oliver Schabenberger, PhD Executive Vice President, Chief Operating Officer and Chief Technology Officer SAS Dive into deep learning! Machine learning and deep learning are ubiquitous in our homes and workplaces-from machine translation to image recognition and predictive analytics to autonomous driving. Deep learning holds the promise of improving many everyday tasks in a variety of disciplines. Much deep learning literature explains the mechanics of deep learning with the goal of implementing cognitive applications fueled by Big Data. This book is different. Written by an expert in high-performance analytics, Deep Learning for Numerical Applications with SAS introduces a new field: Deep Learning for Numerical Applications (DL4NA). Contrary to deep learning, the primary goal of DL4NA is not to learn from data but to dramatically improve the performance of numerical applications by training deep neural networks. Deep Learning for Numerical Applications with SAS presents deep learning concepts in SAS along with step-by-step techniques that allow you to easily reproduce the examples on your high-performance analytics systems. It also discusses the latest hardware innovations that can power your SAS programs: from many-core CPUs to GPUs to FPGAs to ASICs. This book assumes the reader has no prior knowledge of high-performance computing, machine learning, or deep learning. It is intended for SAS developers who want to develop and run the fastest analytics. In addition to discovering the latest trends in hybrid architectures with GPUs and FPGAS, readers will learn how to Use deep learning in SAS Speed up their analytics using deep learning Easily write highly parallel programs using the many task computing paradigms


Book Synopsis Deep Learning for Numerical Applications with SAS (Hardcover Edition) by : Henry Bequet

Download or read book Deep Learning for Numerical Applications with SAS (Hardcover Edition) written by Henry Bequet and published by . This book was released on 2019-08-16 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: Foreword by Oliver Schabenberger, PhD Executive Vice President, Chief Operating Officer and Chief Technology Officer SAS Dive into deep learning! Machine learning and deep learning are ubiquitous in our homes and workplaces-from machine translation to image recognition and predictive analytics to autonomous driving. Deep learning holds the promise of improving many everyday tasks in a variety of disciplines. Much deep learning literature explains the mechanics of deep learning with the goal of implementing cognitive applications fueled by Big Data. This book is different. Written by an expert in high-performance analytics, Deep Learning for Numerical Applications with SAS introduces a new field: Deep Learning for Numerical Applications (DL4NA). Contrary to deep learning, the primary goal of DL4NA is not to learn from data but to dramatically improve the performance of numerical applications by training deep neural networks. Deep Learning for Numerical Applications with SAS presents deep learning concepts in SAS along with step-by-step techniques that allow you to easily reproduce the examples on your high-performance analytics systems. It also discusses the latest hardware innovations that can power your SAS programs: from many-core CPUs to GPUs to FPGAs to ASICs. This book assumes the reader has no prior knowledge of high-performance computing, machine learning, or deep learning. It is intended for SAS developers who want to develop and run the fastest analytics. In addition to discovering the latest trends in hybrid architectures with GPUs and FPGAS, readers will learn how to Use deep learning in SAS Speed up their analytics using deep learning Easily write highly parallel programs using the many task computing paradigms