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Author: Venkata Sathya Kumar koppisetti
Publisher: RK Publication
ISBN: 819778115X
Category : Computers
Languages : en
Pages : 314
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Book Description
Computer Vision and Image Recognition transformative technology enabling machines to interpret and understand visual information. This book explores the foundational theories and techniques in computer vision, covering critical topics such as image processing, feature extraction, object detection, and classification. With applications spanning from autonomous vehicles to medical imaging, it provides a comprehensive overview of algorithms and deep learning methods that power visual perception in machines. Aimed at students, researchers, and practitioners, this guide bridges theoretical concepts with real-world applications, emphasizing advancements in AI-driven image recognition and the future of intelligent visual systems.
Author: Venkata Sathya Kumar koppisetti
Publisher: RK Publication
ISBN: 819778115X
Category : Computers
Languages : en
Pages : 314
Get Book Here
Book Description
Computer Vision and Image Recognition transformative technology enabling machines to interpret and understand visual information. This book explores the foundational theories and techniques in computer vision, covering critical topics such as image processing, feature extraction, object detection, and classification. With applications spanning from autonomous vehicles to medical imaging, it provides a comprehensive overview of algorithms and deep learning methods that power visual perception in machines. Aimed at students, researchers, and practitioners, this guide bridges theoretical concepts with real-world applications, emphasizing advancements in AI-driven image recognition and the future of intelligent visual systems.
Author: Abhishek Mishra
Publisher: John Wiley & Sons
ISBN: 1119556724
Category : Computers
Languages : en
Pages : 696
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Book Description
Put the power of AWS Cloud machine learning services to work in your business and commercial applications! Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabilities of the Amazon Web Services ecosystem and provides practical examples to solve real-world regression and classification problems. While readers do not need prior ML experience, they are expected to have some knowledge of Python and a basic knowledge of Amazon Web Services. Part One introduces readers to fundamental machine learning concepts. You will learn about the types of ML systems, how they are used, and challenges you may face with ML solutions. Part Two focuses on machine learning services provided by Amazon Web Services. You’ll be introduced to the basics of cloud computing and AWS offerings in the cloud-based machine learning space. Then you’ll learn to use Amazon Machine Learning to solve a simpler class of machine learning problems, and Amazon SageMaker to solve more complex problems. • Learn techniques that allow you to preprocess data, basic feature engineering, visualizing data, and model building • Discover common neural network frameworks with Amazon SageMaker • Solve computer vision problems with Amazon Rekognition • Benefit from illustrations, source code examples, and sidebars in each chapter The book appeals to both Python developers and technical/solution architects. Developers will find concrete examples that show them how to perform common ML tasks with Python on AWS. Technical/solution architects will find useful information on the machine learning capabilities of the AWS ecosystem.
Author: Parul Dubey
Publisher: CRC Press
ISBN: 1003824617
Category : Computers
Languages : en
Pages : 193
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Book Description
Amazon Web Services (AWS) provides on-demand cloud computing platforms and application programming interfaces (APIs) to individuals, companies, and govern- ments, along with distributed computing processing capacity and software tools via AWS server farms. This text presents a hands-on approach for beginners to get started with Amazon Web Services (AWS) in a simple way. Key Features It discusses topics such as Amazon Elastic Compute Cloud, Elastic Load Balancing, Auto Scaling Groups, and Amazon Simple Storage Service. It showcases Amazon Web Services’ identity, access management resources, and attribute-based access control. It covers serverless computing services, Virtual Private Cloud, Amazon Aurora, and Amazon Comprehend. It explains Amazon Web Services Free Tier, Amazon Web Services Marketplace, and Amazon Elastic Container Service. It includes security in Amazon Web Services, the shared responsibilitymodel, and high-performance computing on Amazon Web Services. The text is primarily written for graduate students, professionals, and academic researchers working in the fields of computer science, engineering, and information technology. Parul Dubey is currently working as an Assistant professor in the Department of Artificial Intelligence at G H Raisoni College of Engineering, Nagpur, India. She has filed for 15 Indian patents. She is responsible for about 10 publications in conference proceedings, Scopus, and journals. She has contributed book chapters in an edited book published by CRC Press and other reputed publishers. She is also an AWS Certified Cloud Practitioner. Rohit Raja is working as an associate professor and head in the Department of Information Technology at Guru Ghasidas Vishwavidyalaya, Bilaspur, India. His research interests include facial recognition, signal processing, networking, and data mining. He has pub- lished 100 research papers in various international and national journals (including publications by the IEEE, Springer, etc.) and proceedings of reputed international and national conferences (again including publications by Springer and the IEEE).
Author: Krishnendu Kar
Publisher: Packt Publishing Ltd
ISBN: 1838826939
Category : Computers
Languages : en
Pages : 419
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Book Description
Apply neural network architectures to build state-of-the-art computer vision applications using the Python programming language Key FeaturesGain a fundamental understanding of advanced computer vision and neural network models in use todayCover tasks such as low-level vision, image classification, and object detectionDevelop deep learning models on cloud platforms and optimize them using TensorFlow Lite and the OpenVINO toolkitBook Description Computer vision allows machines to gain human-level understanding to visualize, process, and analyze images and videos. This book focuses on using TensorFlow to help you learn advanced computer vision tasks such as image acquisition, processing, and analysis. You'll start with the key principles of computer vision and deep learning to build a solid foundation, before covering neural network architectures and understanding how they work rather than using them as a black box. Next, you'll explore architectures such as VGG, ResNet, Inception, R-CNN, SSD, YOLO, and MobileNet. As you advance, you'll learn to use visual search methods using transfer learning. You'll also cover advanced computer vision concepts such as semantic segmentation, image inpainting with GAN's, object tracking, video segmentation, and action recognition. Later, the book focuses on how machine learning and deep learning concepts can be used to perform tasks such as edge detection and face recognition. You'll then discover how to develop powerful neural network models on your PC and on various cloud platforms. Finally, you'll learn to perform model optimization methods to deploy models on edge devices for real-time inference. By the end of this book, you'll have a solid understanding of computer vision and be able to confidently develop models to automate tasks. What you will learnExplore methods of feature extraction and image retrieval and visualize different layers of the neural network modelUse TensorFlow for various visual search methods for real-world scenariosBuild neural networks or adjust parameters to optimize the performance of modelsUnderstand TensorFlow DeepLab to perform semantic segmentation on images and DCGAN for image inpaintingEvaluate your model and optimize and integrate it into your application to operate at scaleGet up to speed with techniques for performing manual and automated image annotationWho this book is for This book is for computer vision professionals, image processing professionals, machine learning engineers and AI developers who have some knowledge of machine learning and deep learning and want to build expert-level computer vision applications. In addition to familiarity with TensorFlow, Python knowledge will be required to get started with this book.
Author: Sunila Gollapudi
Publisher: Apress
ISBN: 1484242610
Category : Computers
Languages : en
Pages : 163
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Book Description
Build practical applications of computer vision using the OpenCV library with Python. This book discusses different facets of computer vision such as image and object detection, tracking and motion analysis and their applications with examples. The author starts with an introduction to computer vision followed by setting up OpenCV from scratch using Python. The next section discusses specialized image processing and segmentation and how images are stored and processed by a computer. This involves pattern recognition and image tagging using the OpenCV library. Next, you’ll work with object detection, video storage and interpretation, and human detection using OpenCV. Tracking and motion is also discussed in detail. The book also discusses creating complex deep learning models with CNN and RNN. The author finally concludes with recent applications and trends in computer vision. After reading this book, you will be able to understand and implement computer vision and its applications with OpenCV using Python. You will also be able to create deep learning models with CNN and RNN and understand how these cutting-edge deep learning architectures work. What You Will LearnUnderstand what computer vision is, and its overall application in intelligent automation systems Discover the deep learning techniques required to build computer vision applications Build complex computer vision applications using the latest techniques in OpenCV, Python, and NumPy Create practical applications and implementations such as face detection and recognition, handwriting recognition, object detection, and tracking and motion analysis Who This Book Is ForThose who have a basic understanding of machine learning and Python and are looking to learn computer vision and its applications.
Author: Noah Gift
Publisher: "O'Reilly Media, Inc."
ISBN: 1098102967
Category : Computers
Languages : en
Pages : 467
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Book Description
Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models. Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start. You'll discover how to: Apply DevOps best practices to machine learning Build production machine learning systems and maintain them Monitor, instrument, load-test, and operationalize machine learning systems Choose the correct MLOps tools for a given machine learning task Run machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware
Author: John Culkin
Publisher: "O'Reilly Media, Inc."
ISBN: 1492092576
Category : Computers
Languages : en
Pages : 355
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Book Description
This practical guide provides over 100 self-contained recipes to help you creatively solve issues you may encounter in your AWS cloud endeavors. If you're comfortable with rudimentary scripting and general cloud concepts, this cookbook will give you what you need to both address foundational tasks and create high-level capabilities. AWS Cookbook provides real-world examples that incorporate best practices. Each recipe includes code that you can safely execute in a sandbox AWS account to ensure that it works. From there, you can customize the code to help construct your application or fix your specific existing problem. Recipes also include a discussion that explains the approach and provides context. This cookbook takes you beyond theory, providing the nuts and bolts you need to successfully build on AWS. You'll find recipes for: Organizing multiple accounts for enterprise deployments Locking down S3 buckets Analyzing IAM roles Autoscaling a containerized service Summarizing news articles Standing up a virtual call center Creating a chatbot that can pull answers from a knowledge repository Automating security group rule monitoring, looking for rogue traffic flows And more.
Author: V Kishore Ayyadevara
Publisher: Packt Publishing Ltd
ISBN: 1839216530
Category : Computers
Languages : en
Pages : 805
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Book Description
Get to grips with deep learning techniques for building image processing applications using PyTorch with the help of code notebooks and test questions Key FeaturesImplement solutions to 50 real-world computer vision applications using PyTorchUnderstand the theory and working mechanisms of neural network architectures and their implementationDiscover best practices using a custom library created especially for this bookBook Description Deep learning is the driving force behind many recent advances in various computer vision (CV) applications. This book takes a hands-on approach to help you to solve over 50 CV problems using PyTorch1.x on real-world datasets. You’ll start by building a neural network (NN) from scratch using NumPy and PyTorch and discover best practices for tweaking its hyperparameters. You’ll then perform image classification using convolutional neural networks and transfer learning and understand how they work. As you progress, you’ll implement multiple use cases of 2D and 3D multi-object detection, segmentation, human-pose-estimation by learning about the R-CNN family, SSD, YOLO, U-Net architectures, and the Detectron2 platform. The book will also guide you in performing facial expression swapping, generating new faces, and manipulating facial expressions as you explore autoencoders and modern generative adversarial networks. You’ll learn how to combine CV with NLP techniques, such as LSTM and transformer, and RL techniques, such as Deep Q-learning, to implement OCR, image captioning, object detection, and a self-driving car agent. Finally, you'll move your NN model to production on the AWS Cloud. By the end of this book, you’ll be able to leverage modern NN architectures to solve over 50 real-world CV problems confidently. What you will learnTrain a NN from scratch with NumPy and PyTorchImplement 2D and 3D multi-object detection and segmentationGenerate digits and DeepFakes with autoencoders and advanced GANsManipulate images using CycleGAN, Pix2PixGAN, StyleGAN2, and SRGANCombine CV with NLP to perform OCR, image captioning, and object detectionCombine CV with reinforcement learning to build agents that play pong and self-drive a carDeploy a deep learning model on the AWS server using FastAPI and DockerImplement over 35 NN architectures and common OpenCV utilitiesWho this book is for This book is for beginners to PyTorch and intermediate-level machine learning practitioners who are looking to get well-versed with computer vision techniques using deep learning and PyTorch. If you are just getting started with neural networks, you’ll find the use cases accompanied by notebooks in GitHub present in this book useful. Basic knowledge of the Python programming language and machine learning is all you need to get started with this book.
Author: Marco Tranquillin
Publisher: "O'Reilly Media, Inc."
ISBN: 1098151585
Category : Computers
Languages : en
Pages : 361
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Book Description
All cloud architects need to know how to build data platforms that enable businesses to make data-driven decisions and deliver enterprise-wide intelligence in a fast and efficient way. This handbook shows you how to design, build, and modernize cloud native data and machine learning platforms using AWS, Azure, Google Cloud, and multicloud tools like Snowflake and Databricks. Authors Marco Tranquillin, Valliappa Lakshmanan, and Firat Tekiner cover the entire data lifecycle from ingestion to activation in a cloud environment using real-world enterprise architectures. You'll learn how to transform, secure, and modernize familiar solutions like data warehouses and data lakes, and you'll be able to leverage recent AI/ML patterns to get accurate and quicker insights to drive competitive advantage. You'll learn how to: Design a modern and secure cloud native or hybrid data analytics and machine learning platform Accelerate data-led innovation by consolidating enterprise data in a governed, scalable, and resilient data platform Democratize access to enterprise data and govern how business teams extract insights and build AI/ML capabilities Enable your business to make decisions in real time using streaming pipelines Build an MLOps platform to move to a predictive and prescriptive analytics approach
Author: Ernesto Damiani
Publisher: Springer Nature
ISBN: 3030402231
Category : Computers
Languages : en
Pages : 413
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Book Description
This book constitutesselected, revised and extended papers of the 14th International Conference on Evaluation of Novel Approaches to Software Engineering, ENASE 2019, held in Heraklion, Crete, Greece, in May 2019. The 19 revised full papers presented were carefully reviewed and selected from 102 submissions. The papers included in this book contribute to the understanding of relevant trends of current research on novel approaches to software engineering for the development and maintenance of systems and applications, specically with relation to: model-driven software engineering, requirements engineering, empirical software engineering, service-oriented software engineering, business process management and engineering, knowledge management and engineering, reverse software engineering, software process improvement, software change and configuration management, software metrics, software patterns and refactoring, application integration, software architecture, cloud computing, and formal methods.
