Deep Learning for Computer Vision with SAS PDF Download

Author: Robert Blanchard
Publisher: SAS Institute
ISBN: 1642959170
Category : Computers
Languages : en
Pages : 120

View

Book Description
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

Author: Susan Kahler
Publisher:
ISBN: 9781952365041
Category : Computers
Languages : en
Pages : 112

View

Book Description
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.

Author: Henry Bequet
Publisher: SAS Institute
ISBN: 1635266777
Category : Computers
Languages : en
Pages : 234

View

Book Description
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.

Author: SAS Institute Inc.
Publisher: SAS Institute
ISBN: 1951685377
Category : Computers
Languages : en
Pages : 295

View

Book Description
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

Author: Mahmoud Hassaballah
Publisher: CRC Press
ISBN: 1351003801
Category : Computers
Languages : en
Pages : 261

View

Book Description
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.

Author: R. Sujatha
Publisher: Green Engineering and Technology
ISBN: 9780367466633
Category : Algorithms
Languages : en
Pages : 204

View

Book Description
Data science revolves around two giants: Big Data analytics and Deep Learning. It is becoming challenging to handle and retrieve useful information due to how fast data is expanding. This book presents the technologies and tools to simplify and streamline the formation of Big Data as well as Deep Learning systems. This book discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and decision-making. It also covers numerous applications in healthcare, education, communication, media, and entertainment. Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics offers innovative platforms for integrating Big Data and Deep Learning and presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval. FEATURES Provides insight into the skill set that leverages one's strength to act as a good data analyst Discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and help in decision-making Covers numerous potential applications in healthcare, education, communication, media, and entertainment Offers innovative platforms for integrating Big Data and Deep Learning Presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval from Big Data This book is aimed at industry professionals, academics, research scholars, system modelers, and simulation experts.

Author: Jyotsnarani Tripathy
Publisher: Leilani Katie Publication
ISBN: 9363484777
Category : Computers
Languages : en
Pages : 201

View

Book Description
Jyotsnarani Tripathy, Assistant Professor, Department of CSE-AIML & IoT, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology (VNRVJIET), Hyderabad, Telangana, India. Dr.M.Kamal, Assistant Professor, Department of Computer Science, Jamal Mohamed College (Autonomous), Tiruchirappalli, Tamil Nadu, India. G.Ashalatha, Assistant Professor, Department of Artificial Intelligence & Data Science, CSE-Cyber Security, Data Science, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering &Technology (VNR VJIET), Hyderabad, Telangana, India. Mrs.EMN.Sharmila, Research Scholar, Department of Computer Science, CIRD Research Centre (Approved by University of Mysore), Bengaluru, Karnataka, India.

Author: Krishna Kant Singh
Publisher: CRC Press
ISBN: 1000564886
Category : Science
Languages : en
Pages : 270

View

Book Description
An enlightening amalgamation of deep learning concepts with visual computing and signal processing applications, this new volume covers the fundamentals and advanced topics in designing and deploying techniques using deep architectures and their application in visual computing and signal processing. The volume first lays out the fundamentals of deep learning as well as deep learning architectures and frameworks. It goes on to discuss deep learning in neural networks and deep learning for object recognition and detection models. It looks at the various specific applications of deep learning in visual and signal processing, such as in biorobotics, for automated brain tumor segmentation in MRI images, in neural networks for use in seizure classification, for digital forensic investigation based on deep learning, and more.

Author: Singh Nikhil
Publisher: BPB Publications
ISBN: 9388176618
Category : Computers
Languages : en
Pages : 227

View

Book Description
Master Computer Vision concepts using Deep Learning with easy-to-follow steps Key Featuresa- Setting up the Python and TensorFlow environmenta- Learn core Tensorflow concepts with the latest TF version 2.0a- Learn Deep Learning for computer vision applications a- Understand different computer vision concepts and use-casesa- Understand different state-of-the-art CNN architectures a- Build deep neural networks with transfer Learning using features from pre-trained CNN modelsa- Apply computer vision concepts with easy-to-follow code in Jupyter NotebookDescriptionThis book starts with setting up a Python virtual environment with the deep learning framework TensorFlow and then introduces the fundamental concepts of TensorFlow. Before moving on to Computer Vision, you will learn about neural networks and related aspects such as loss functions, gradient descent optimization, activation functions and how backpropagation works for training multi-layer perceptrons.To understand how the Convolutional Neural Network (CNN) is used for computer vision problems, you need to learn about the basic convolution operation. You will learn how CNN is different from a multi-layer perceptron along with a thorough discussion on the different building blocks of the CNN architecture such as kernel size, stride, padding, and pooling and finally learn how to build a small CNN model. Next, you will learn about different popular CNN architectures such as AlexNet, VGGNet, Inception, and ResNets along with different object detection algorithms such as RCNN, SSD, and YOLO. The book concludes with a chapter on sequential models where you will learn about RNN, GRU, and LSTMs and their architectures and understand their applications in machine translation, image/video captioning and video classification.What will you learnThis book will help the readers to understand and apply the latest Deep Learning technologies to different interesting computer vision applications without any prior domain knowledge of image processing. Thus, helping the users to acquire new skills specific to Computer Vision and Deep Learning and build solutions to real-life problems such as Image Classification and Object Detection. Who this book is forThis book is for all the Data Science enthusiasts and practitioners who intend to learn and master Computer Vision concepts and their applications using Deep Learning. This book assumes a basic Python understanding with hands-on experience. A basic senior secondary level understanding of Mathematics will help the reader to make the best out of this book. Table of Contents1. Introduction to TensorFlow2. Introduction to Neural Networks 3. Convolutional Neural Network 4. CNN Architectures5. Sequential ModelsAbout the AuthorNikhil Singh is an accomplished data scientist and currently working as the Lead Data Scientist at Proarch IT Solutions Pvt. Ltd in London. He has experience in designing and delivering complex and innovative computer vision and NLP centred solutions for a large number of global companies. He has been an AI consultant to a few companies and mentored many apprentice Data Scientists. His LinkedIn Profile: https://www.linkedin.com/in/nikhil-singh-b953ba122/Paras Ahuja is a seasoned data science practitioner and currently working as the Lead Data Scientist at Reliance Jio in Hyderabad. He has good experience in designing and deploying deep learning-based Computer Vision and NLP-based solutions. He has experience in developing and implementing state-of-the-art automatic speech recognition systems.His LinkedIn Profile: https://www.linkedin.com/in/parasahuja

Author: Nikhil Singh
Publisher: BPB Publications
ISBN: 9388511859
Category : Computers
Languages : en
Pages : 222

View

Book Description
Master Computer Vision concepts using Deep Learning with easy-to-follow steps DESCRIPTIONÊ This book starts with setting up a Python virtual environment with the deep learning framework TensorFlow and then introduces the fundamental concepts of TensorFlow. Before moving on to Computer Vision, you will learn about neural networks and related aspects such as loss functions, gradient descent optimization, activation functions and how backpropagation works for training multi-layer perceptrons. To understand how the Convolutional Neural Network (CNN) is used for computer vision problems, you need to learn about the basic convolution operation. You will learn how CNN is different from a multi-layer perceptron along with a thorough discussion on the different building blocks of the CNN architecture such as kernel size, stride, padding, and pooling and finally learn how to build a small CNN model.Ê Next, you will learn about different popular CNN architectures such as AlexNet, VGGNet, Inception, and ResNets along with different object detection algorithms such as RCNN, SSD, and YOLO. The book concludes with a chapter on sequential models where you will learn about RNN, GRU, and LSTMs and their architectures and understand their applications in machine translation, image/video captioning and video classification. KEY FEATURESÊ Setting up the Python and TensorFlow environment Learn core Tensorflow concepts with the latest TF version 2.0 Learn Deep Learning for computer vision applicationsÊ Understand different computer vision concepts and use-cases Understand different state-of-the-art CNN architecturesÊ Build deep neural networks with transfer Learning using features from pre-trained CNN models Apply computer vision concepts with easy-to-follow code in Jupyter Notebook WHAT WILL YOU LEARNÊ This book will help the readers to understand and apply the latest Deep Learning technologies to different interesting computer vision applications without any prior domain knowledge of image processing. Thus, helping the users to acquire new skills specific to Computer Vision and Deep Learning and build solutions to real-life problems such as Image Classification and Object Detection. This book will serve as a basic guide for all the beginners to master Deep Learning and Computer Vision with lucid and intuitive explanations using basic mathematical concepts. It also explores these concepts with popular the deep learning framework TensorFlow. WHO THIS BOOK IS FOR This book is for all the Data Science enthusiasts and practitioners who intend to learn and master Computer Vision concepts and their applications using Deep Learning. This book assumes a basic Python understanding with hands-on experience. A basic senior secondary level understanding of Mathematics will help the reader to make the best out of this book.Ê Table of Contents 1. Introduction to TensorFlow 2. Introduction to Neural NetworksÊ 3. Convolutional Neural NetworkÊÊ 4. CNN Architectures 5. Sequential Models