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Deep Learning for Computer Vision with SAS

Deep Learning for Computer Vision with SAS
Author: Robert Blanchard
Publisher: SAS Institute
Total Pages: 120
Release: 2020-06-12
Genre: Computers
ISBN: 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


Deep Learning for Computer Vision with SAS

Deep Learning for Computer Vision with SAS
Author: Robert Blanchard
Publisher:
Total Pages: 150
Release: 2020-06-12
Genre:
ISBN: 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


Computer Vision with SAS

Computer Vision with SAS
Author: Susan Kahler
Publisher:
Total Pages: 112
Release: 2020-07-22
Genre: Computers
ISBN: 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.


Deep Learning in Computer Vision

Deep Learning in Computer Vision
Author: Mahmoud Hassaballah
Publisher: CRC Press
Total Pages: 261
Release: 2020-03-23
Genre: Computers
ISBN: 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.


Machine Learning with SAS Viya

Machine Learning with SAS Viya
Author: SAS Institute Inc.
Publisher: SAS Institute
Total Pages: 295
Release: 2020-05-29
Genre: Computers
ISBN: 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


Deep Learning for Computer Vision

Deep Learning for Computer Vision
Author: Rajalingappaa Shanmugamani
Publisher:
Total Pages:
Release: 2018
Genre: COMPUTERS
ISBN: 9781523116751

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Deep Learning for Numerical Applications with SAS

Deep Learning for Numerical Applications with SAS
Author: Henry Bequet
Publisher: SAS Institute
Total Pages: 234
Release: 2018-07-20
Genre: Computers
ISBN: 1635266777

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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 This book is part of the SAS Press program.


Computer Vision Using Deep Learning

Computer Vision Using Deep Learning
Author: Vaibhav Verdhan
Publisher: Apress
Total Pages: 308
Release: 2021-02-15
Genre: Computers
ISBN: 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.


Advanced Methods and Deep Learning in Computer Vision

Advanced Methods and Deep Learning in Computer Vision
Author: E. R. Davies
Publisher: Academic Press
Total Pages: 584
Release: 2021-11-09
Genre: Computers
ISBN: 0128221496

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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


Deep Learning for Computer Vision

Deep Learning for Computer Vision
Author: Jason Brownlee
Publisher: Machine Learning Mastery
Total Pages: 564
Release: 2019-04-04
Genre: Computers
ISBN:

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Step-by-step tutorials on deep learning neural networks for computer vision in python with Keras.