Applied Cloud Deep Semantic Recognition

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Applied Cloud Deep Semantic Recognition

Author : Mehdi Roopaei
Publisher : CRC Press
ISBN 13 : 1351119001
Total Pages : 239 pages
Book Rating : 4.3/5 (511 download)

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Book Synopsis Applied Cloud Deep Semantic Recognition by : Mehdi Roopaei

Download or read book Applied Cloud Deep Semantic Recognition written by Mehdi Roopaei and published by CRC Press. This book was released on 2018-04-09 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive overview of the research on anomaly detection with respect to context and situational awareness that aim to get a better understanding of how context information influences anomaly detection. In each chapter, it identifies advanced anomaly detection and key assumptions, which are used by the model to differentiate between normal and anomalous behavior. When applying a given model to a particular application, the assumptions can be used as guidelines to assess the effectiveness of the model in that domain. Each chapter provides an advanced deep content understanding and anomaly detection algorithm, and then shows how the proposed approach is deviating of the basic techniques. Further, for each chapter, it describes the advantages and disadvantages of the algorithm. The final chapters provide a discussion on the computational complexity of the models and graph computational frameworks such as Google Tensorflow and H2O because it is an important issue in real application domains. This book provides a better understanding of the different directions in which research has been done on deep semantic analysis and situational assessment using deep learning for anomalous detection, and how methods developed in one area can be applied in applications in other domains. This book seeks to provide both cyber analytics practitioners and researchers an up-to-date and advanced knowledge in cloud based frameworks for deep semantic analysis and advanced anomaly detection using cognitive and artificial intelligence (AI) models.

Applied Cloud Deep Semantic Recognition

Author : Mehdi Roopaei
Publisher : CRC Press
ISBN 13 : 135111901X
Total Pages : 188 pages
Book Rating : 4.3/5 (511 download)

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Book Synopsis Applied Cloud Deep Semantic Recognition by : Mehdi Roopaei

Download or read book Applied Cloud Deep Semantic Recognition written by Mehdi Roopaei and published by CRC Press. This book was released on 2018-04-09 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive overview of the research on anomaly detection with respect to context and situational awareness that aim to get a better understanding of how context information influences anomaly detection. In each chapter, it identifies advanced anomaly detection and key assumptions, which are used by the model to differentiate between normal and anomalous behavior. When applying a given model to a particular application, the assumptions can be used as guidelines to assess the effectiveness of the model in that domain. Each chapter provides an advanced deep content understanding and anomaly detection algorithm, and then shows how the proposed approach is deviating of the basic techniques. Further, for each chapter, it describes the advantages and disadvantages of the algorithm. The final chapters provide a discussion on the computational complexity of the models and graph computational frameworks such as Google Tensorflow and H2O because it is an important issue in real application domains. This book provides a better understanding of the different directions in which research has been done on deep semantic analysis and situational assessment using deep learning for anomalous detection, and how methods developed in one area can be applied in applications in other domains. This book seeks to provide both cyber analytics practitioners and researchers an up-to-date and advanced knowledge in cloud based frameworks for deep semantic analysis and advanced anomaly detection using cognitive and artificial intelligence (AI) models.

Monetizing Machine Learning

Author : Manuel Amunategui
Publisher : Apress
ISBN 13 : 1484238737
Total Pages : 510 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Monetizing Machine Learning by : Manuel Amunategui

Download or read book Monetizing Machine Learning written by Manuel Amunategui and published by Apress. This book was released on 2018-09-12 with total page 510 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take your Python machine learning ideas and create serverless web applications accessible by anyone with an Internet connection. Some of the most popular serverless cloud providers are covered in this book—Amazon, Microsoft, Google, and PythonAnywhere. You will work through a series of common Python data science problems in an increasing order of complexity. The practical projects presented in this book are simple, clear, and can be used as templates to jump-start many other types of projects. You will learn to create a web application around numerical or categorical predictions, understand the analysis of text, create powerful and interactive presentations, serve restricted access to data, and leverage web plugins to accept credit card payments and donations. You will get your projects into the hands of the world in no time. Each chapter follows three steps: modeling the right way, designing and developing a local web application, and deploying onto a popular and reliable serverless cloud provider. You can easily jump to or skip particular topics in the book. You also will have access to Jupyter notebooks and code repositories for complete versions of the code covered in the book. What You’ll Learn Extend your machine learning models using simple techniques to create compelling and interactive web dashboards Leverage the Flask web framework for rapid prototyping of your Python models and ideasCreate dynamic content powered by regression coefficients, logistic regressions, gradient boosting machines, Bayesian classifications, and more Harness the power of TensorFlow by exporting saved models into web applications Create rich web dashboards to handle complex real-time user input with JavaScript and Ajax to yield interactive and tailored contentCreate dashboards with paywalls to offer subscription-based accessAccess API data such as Google Maps, OpenWeather, etc.Apply different approaches to make sense of text data and return customized intelligence Build an intuitive and useful recommendation site to add value to users and entice them to keep coming back Utilize the freemium offerings of Google Analytics and analyze the results Take your ideas all the way to your customer's plate using the top serverless cloud providers Who This Book Is For Those with some programming experience with Python, code editing, and access to an interpreter in working order. The book is geared toward entrepreneurs who want to get their ideas onto the web without breaking the bank, small companies without an IT staff, students wanting exposure and training, and for all data science professionals ready to take things to the next level.

Deep Learning through Sparse and Low-Rank Modeling

Author : Zhangyang Wang
Publisher : Academic Press
ISBN 13 : 0128136596
Total Pages : 296 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Deep Learning through Sparse and Low-Rank Modeling by : Zhangyang Wang

Download or read book Deep Learning through Sparse and Low-Rank Modeling written by Zhangyang Wang and published by Academic Press. This book was released on 2019-04-26 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models-those that emphasize problem-specific Interpretability-with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics. Combines classical sparse and low-rank models and algorithms with the latest advances in deep learning networks Shows how the structure and algorithms of sparse and low-rank methods improves the performance and interpretability of Deep Learning models Provides tactics on how to build and apply customized deep learning models for various applications

Computer Vision – ECCV 2018 Workshops

Author : Laura Leal-Taixé
Publisher : Springer
ISBN 13 : 3030110249
Total Pages : 743 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Computer Vision – ECCV 2018 Workshops by : Laura Leal-Taixé

Download or read book Computer Vision – ECCV 2018 Workshops written by Laura Leal-Taixé and published by Springer. This book was released on 2019-01-22 with total page 743 pages. Available in PDF, EPUB and Kindle. Book excerpt: The six-volume set comprising the LNCS volumes 11129-11134 constitutes the refereed proceedings of the workshops that took place in conjunction with the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.43 workshops from 74 workshops proposals were selected for inclusion in the proceedings. The workshop topics present a good orchestration of new trends and traditional issues, built bridges into neighboring fields, and discuss fundamental technologies and novel applications.

Intelligent Systems and Human Machine Collaboration

Author : Siddhartha Bhattacharyya
Publisher : Springer Nature
ISBN 13 : 9811984778
Total Pages : 280 pages
Book Rating : 4.8/5 (119 download)

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Book Synopsis Intelligent Systems and Human Machine Collaboration by : Siddhartha Bhattacharyya

Download or read book Intelligent Systems and Human Machine Collaboration written by Siddhartha Bhattacharyya and published by Springer Nature. This book was released on 2023-03-29 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book constitutes proceedings of the International Conference on Intelligent Systems and Human-Machine Collaboration 2022. The papers consist of research from different domains of human-machine interaction, computer engineering like quantum computational intelligence, big data analytics, the Internet of things, etc. The book includes significant contributions from academia and industry dealing with human-machine interaction both from the theoretical development and the application point of view. It also brings out research articles in interdisciplinary platforms applying human-machine interaction. The book is useful to researchers and practitioners alike.

E-Life: Web-Enabled Convergence of Commerce, Work, and Social Life

Author : Vijayan Sugumaran
Publisher : Springer
ISBN 13 : 3319454080
Total Pages : 240 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis E-Life: Web-Enabled Convergence of Commerce, Work, and Social Life by : Vijayan Sugumaran

Download or read book E-Life: Web-Enabled Convergence of Commerce, Work, and Social Life written by Vijayan Sugumaran and published by Springer. This book was released on 2016-08-31 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Workshop on E-Business (WeB 2015), held in Fort Worth, Texas, USA, on December 12, 2015. The theme of WeB 2015 was “Leveraging Service Computing and Big Data Analytics for E-Commerce”, and thus the workshop provided an interactive forum by bringing together researchers and practitioners from all over the world to explore the latest challenges of next-generation e-Business systems and the potential of service computing and big data analytics. The 11 full and 17 short papers, which were selected from 45 submissions to the workshop, addressed a broad coverage of technical, managerial, economic, and strategic issues related to e-business, with emphasis on service computing and big data analytics. They employed various IS research methods such as case study, survey, analytical modeling, experiments, computational models, and design science.

Towards Autonomous Robotic Systems

Author : Charles Fox
Publisher : Springer Nature
ISBN 13 : 3030891771
Total Pages : 451 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Towards Autonomous Robotic Systems by : Charles Fox

Download or read book Towards Autonomous Robotic Systems written by Charles Fox and published by Springer Nature. This book was released on 2021-10-30 with total page 451 pages. Available in PDF, EPUB and Kindle. Book excerpt: The volume LNAI 13054 constitutes the refereed proceedings of the 22th Annual Conference Towards Autonomous Robotic Systems, TAROS 2021, held in Lincoln, UK, in September 2021.*The 45 full papers were carefully reviewed and selected from 66 submissions. Organized in the topical sections "Algorithms" and "Systems", they discuss significant findings and advances in the following areas: artificial intelligence; mechatronics; image processing and computer vision; special purpose and application-based systems; user interfaces and human computer interaction. * The conference was held virtually due to the COVID-19 pandemic.

Deep Learning Approaches to Cloud Security

Author : Pramod Singh Rathore
Publisher : John Wiley & Sons
ISBN 13 : 1119760526
Total Pages : 308 pages
Book Rating : 4.1/5 (197 download)

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Book Synopsis Deep Learning Approaches to Cloud Security by : Pramod Singh Rathore

Download or read book Deep Learning Approaches to Cloud Security written by Pramod Singh Rathore and published by John Wiley & Sons. This book was released on 2022-01-26 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: DEEP LEARNING APPROACHES TO CLOUD SECURITY Covering one of the most important subjects to our society today, cloud security, this editorial team delves into solutions taken from evolving deep learning approaches, solutions allowing computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined through its relation to simpler concepts. Deep learning is the fastest growing field in computer science. Deep learning algorithms and techniques are found to be useful in different areas like automatic machine translation, automatic handwriting generation, visual recognition, fraud detection, and detecting developmental delay in children. However, applying deep learning techniques or algorithms successfully in these areas needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. This book provides state of the art approaches of deep learning in these areas, including areas of detection and prediction, as well as future framework development, building service systems and analytical aspects. In all these topics, deep learning approaches, such as artificial neural networks, fuzzy logic, genetic algorithms, and hybrid mechanisms are used. This book is intended for dealing with modeling and performance prediction of the efficient cloud security systems, thereby bringing a newer dimension to this rapidly evolving field. This groundbreaking new volume presents these topics and trends of deep learning, bridging the research gap, and presenting solutions to the challenges facing the engineer or scientist every day in this area. Whether for the veteran engineer or the student, this is a must-have for any library. Deep Learning Approaches to Cloud Security: Is the first volume of its kind to go in-depth on the newest trends and innovations in cloud security through the use of deep learning approaches Covers these important new innovations, such as AI, data mining, and other evolving computing technologies in relation to cloud security Is a useful reference for the veteran computer scientist or engineer working in this area or an engineer new to the area, or a student in this area Discusses not just the practical applications of these technologies, but also the broader concepts and theory behind how these deep learning tools are vital not just to cloud security, but society as a whole Audience: Computer scientists, scientists and engineers working with information technology, design, network security, and manufacturing, researchers in computers, electronics, and electrical and network security, integrated domain, and data analytics, and students in these areas

Applied Deep Learning and Computer Vision for Self-Driving Cars

Author : Sumit Ranjan
Publisher : Packt Publishing Ltd
ISBN 13 : 1838647023
Total Pages : 320 pages
Book Rating : 4.8/5 (386 download)

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Book Synopsis Applied Deep Learning and Computer Vision for Self-Driving Cars by : Sumit Ranjan

Download or read book Applied Deep Learning and Computer Vision for Self-Driving Cars written by Sumit Ranjan and published by Packt Publishing Ltd. This book was released on 2020-08-14 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore self-driving car technology using deep learning and artificial intelligence techniques and libraries such as TensorFlow, Keras, and OpenCV Key FeaturesBuild and train powerful neural network models to build an autonomous carImplement computer vision, deep learning, and AI techniques to create automotive algorithmsOvercome the challenges faced while automating different aspects of driving using modern Python libraries and architecturesBook Description Thanks to a number of recent breakthroughs, self-driving car technology is now an emerging subject in the field of artificial intelligence and has shifted data scientists' focus to building autonomous cars that will transform the automotive industry. This book is a comprehensive guide to use deep learning and computer vision techniques to develop autonomous cars. Starting with the basics of self-driving cars (SDCs), this book will take you through the deep neural network techniques required to get up and running with building your autonomous vehicle. Once you are comfortable with the basics, you'll delve into advanced computer vision techniques and learn how to use deep learning methods to perform a variety of computer vision tasks such as finding lane lines, improving image classification, and so on. You will explore the basic structure and working of a semantic segmentation model and get to grips with detecting cars using semantic segmentation. The book also covers advanced applications such as behavior-cloning and vehicle detection using OpenCV, transfer learning, and deep learning methodologies to train SDCs to mimic human driving. By the end of this book, you'll have learned how to implement a variety of neural networks to develop your own autonomous vehicle using modern Python libraries. What you will learnImplement deep neural network from scratch using the Keras libraryUnderstand the importance of deep learning in self-driving carsGet to grips with feature extraction techniques in image processing using the OpenCV libraryDesign a software pipeline that detects lane lines in videosImplement a convolutional neural network (CNN) image classifier for traffic signal signsTrain and test neural networks for behavioral-cloning by driving a car in a virtual simulatorDiscover various state-of-the-art semantic segmentation and object detection architecturesWho this book is for If you are a deep learning engineer, AI researcher, or anyone looking to implement deep learning and computer vision techniques to build self-driving blueprint solutions, this book is for you. Anyone who wants to learn how various automotive-related algorithms are built, will also find this book useful. Python programming experience, along with a basic understanding of deep learning, is necessary to get the most of this book.

Deep Learning for Computer Vision

Author : Jason Brownlee
Publisher : Machine Learning Mastery
ISBN 13 :
Total Pages : 564 pages
Book Rating : 4./5 ( download)

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Book Synopsis Deep Learning for Computer Vision by : Jason Brownlee

Download or read book Deep Learning for Computer Vision written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2019-04-04 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: Step-by-step tutorials on deep learning neural networks for computer vision in python with Keras.

Library Linked Data in the Cloud

Author : Carol Jean Godby
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1627052208
Total Pages : 156 pages
Book Rating : 4.6/5 (27 download)

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Book Synopsis Library Linked Data in the Cloud by : Carol Jean Godby

Download or read book Library Linked Data in the Cloud written by Carol Jean Godby and published by Morgan & Claypool Publishers. This book was released on 2015-05-25 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes OCLC’s contributions to the transformation of the Internet from a web of documents to a Web of Data. The new Web is a growing ‘cloud’ of interconnected resources that identify the things people want to know about when they approach the Internet with an information need. The linked data architecture has achieved critical mass just as it has become clear that library standards for resource description are nearing obsolescence. Working for the world’s largest library cooperative, OCLC researchers have been active participants in the development of next generation standards for library resource description. By engaging with an international community of library and Web standards experts, they have published some of the most widely used RDF datasets representing library collections and librarianship. This book focuses on the conceptual and technical challenges involved in publishing linked data derived from traditional library metadata. This transformation is a high priority because most searches for information start not in the library, nor even in a Web-accessible library catalog, but elsewhere on the Internet. Modeling data in a form that the broader Web understands will project the value of libraries into the Digital Information Age. The exposition is aimed at librarians, archivists, computer scientists, and other professionals interested in modeling bibliographic descriptions as linked data. It aims to achieve a balanced treatment of theory, technical detail, and practical application.

Applied Machine Learning for Health and Fitness

Author : Kevin Ashley
Publisher : Apress
ISBN 13 : 9781484257715
Total Pages : 0 pages
Book Rating : 4.2/5 (577 download)

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Book Synopsis Applied Machine Learning for Health and Fitness by : Kevin Ashley

Download or read book Applied Machine Learning for Health and Fitness written by Kevin Ashley and published by Apress. This book was released on 2020-08-25 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the world of using machine learning methods with deep computer vision, sensors and data in sports, health and fitness and other industries. Accompanied by practical step-by-step Python code samples and Jupyter notebooks, this comprehensive guide acts as a reference for a data scientist, machine learning practitioner or anyone interested in AI applications. These ML models and methods can be used to create solutions for AI enhanced coaching, judging, athletic performance improvement, movement analysis, simulations, in motion capture, gaming, cinema production and more. Packed with fun, practical applications for sports, machine learning models used in the book include supervised, unsupervised and cutting-edge reinforcement learning methods and models with popular tools like PyTorch, Tensorflow, Keras, OpenAI Gym and OpenCV. Author Kevin Ashley—who happens to be both a machine learning expert and a professional ski instructor—has written an insightful book that takes you on a journey of modern sport science and AI. Filled with thorough, engaging illustrations and dozens of real-life examples, this book is your next step to understanding the implementation of AI within the sports world and beyond. Whether you are a data scientist, a coach, an athlete, or simply a personal fitness enthusiast excited about connecting your findings with AI methods, the author’s practical expertise in both tech and sports is an undeniable asset for your learning process. Today’s data scientists are the future of athletics, and Applied Machine Learning for Health and Fitness hands you the knowledge you need to stay relevant in this rapidly growing space. What You'll Learn Use multiple data science tools and frameworks Apply deep computer vision and other machine learning methods for classification, semantic segmentation, and action recognition Build and train neural networks, reinforcement learning models and more Analyze multiple sporting activities with deep learning Use datasets available today for model training Use machine learning in the cloud to train and deploy models Apply best practices in machine learning and data science Who This Book Is For Primarily aimed at data scientists, coaches, sports enthusiasts and athletes interested in connecting sports with technology and AI methods.

Computer Vision -- ECCV 2010

Author : Kostas Daniilidis
Publisher : Springer Science & Business Media
ISBN 13 : 364215560X
Total Pages : 836 pages
Book Rating : 4.6/5 (421 download)

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Book Synopsis Computer Vision -- ECCV 2010 by : Kostas Daniilidis

Download or read book Computer Vision -- ECCV 2010 written by Kostas Daniilidis and published by Springer Science & Business Media. This book was released on 2010-08-30 with total page 836 pages. Available in PDF, EPUB and Kindle. Book excerpt: The six-volume set comprising LNCS volumes 6311 until 6313 constitutes the refereed proceedings of the 11th European Conference on Computer Vision, ECCV 2010, held in Heraklion, Crete, Greece, in September 2010. The 325 revised papers presented were carefully reviewed and selected from 1174 submissions. The papers are organized in topical sections on object and scene recognition; segmentation and grouping; face, gesture, biometrics; motion and tracking; statistical models and visual learning; matching, registration, alignment; computational imaging; multi-view geometry; image features; video and event characterization; shape representation and recognition; stereo; reflectance, illumination, color; medical image analysis.

Practical Deep Learning for Cloud, Mobile, and Edge

Author : Anirudh Koul
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492034819
Total Pages : 585 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Practical Deep Learning for Cloud, Mobile, and Edge by : Anirudh Koul

Download or read book Practical Deep Learning for Cloud, Mobile, and Edge written by Anirudh Koul and published by "O'Reilly Media, Inc.". This book was released on 2019-10-14 with total page 585 pages. Available in PDF, EPUB and Kindle. Book excerpt: Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning Use transfer learning to train models in minutes Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users

Applied Natural Language Processing in the Enterprise

Author : Ankur A. Patel
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492062545
Total Pages : 336 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Applied Natural Language Processing in the Enterprise by : Ankur A. Patel

Download or read book Applied Natural Language Processing in the Enterprise written by Ankur A. Patel and published by "O'Reilly Media, Inc.". This book was released on 2021-05-12 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: NLP has exploded in popularity over the last few years. But while Google, Facebook, OpenAI, and others continue to release larger language models, many teams still struggle with building NLP applications that live up to the hype. This hands-on guide helps you get up to speed on the latest and most promising trends in NLP. With a basic understanding of machine learning and some Python experience, you'll learn how to build, train, and deploy models for real-world applications in your organization. Authors Ankur Patel and Ajay Uppili Arasanipalai guide you through the process using code and examples that highlight the best practices in modern NLP. Use state-of-the-art NLP models such as BERT and GPT-3 to solve NLP tasks such as named entity recognition, text classification, semantic search, and reading comprehension Train NLP models with performance comparable or superior to that of out-of-the-box systems Learn about Transformer architecture and modern tricks like transfer learning that have taken the NLP world by storm Become familiar with the tools of the trade, including spaCy, Hugging Face, and fast.ai Build core parts of the NLP pipeline--including tokenizers, embeddings, and language models--from scratch using Python and PyTorch Take your models out of Jupyter notebooks and learn how to deploy, monitor, and maintain them in production

Practical Machine Learning for Computer Vision

Author : Valliappa Lakshmanan
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1098102339
Total Pages : 481 pages
Book Rating : 4.0/5 (981 download)

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Book Synopsis Practical Machine Learning for Computer Vision by : Valliappa Lakshmanan

Download or read book Practical Machine Learning for Computer Vision written by Valliappa Lakshmanan and published by "O'Reilly Media, Inc.". This book was released on 2021-07-21 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras. You'll learn how to: Design ML architecture for computer vision tasks Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model Preprocess images for data augmentation and to support learnability Incorporate explainability and responsible AI best practices Deploy image models as web services or on edge devices Monitor and manage ML models