Synthetic Data For Deep Learning

Description: Please refer to the section BELOW (and NOT ABOVE) this line for the product details - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Title:Synthetic Data For Deep LearningISBN13:9783030751777ISBN10:3030751775Author:Nikolenko, Sergey I. (Author)Description:This Is The First Book On Synthetic Data For Deep Learning, And Its Breadth Of Coverage May Render This Book As The Default Reference On Synthetic Data For Years To Come The Book Can Also Serve As An Introduction To Several Other Important Subfields Of Machine Learning That Are Seldom Touched Upon In Other Books Machine Learning As A Discipline Would Not Be Possible Without The Inner Workings Of Optimization At Hand The Book Includes The Necessary Sinews Of Optimization Though The Crux Of The Discussion Centers On The Increasingly Popular Tool For Training Deep Learning Models, Namely Synthetic Data It Is Expected That The Field Of Synthetic Data Will Undergo Exponential Growth In The Near Future This Book Serves As A Comprehensive Survey Of The Field In The Simplest Case, Synthetic Data Refers To Computer-Generated Graphics Used To Train Computer Vision Models There Are Many More Facets Of Synthetic Data To Consider In The Section On Basic Computer Vision, The Book Discusses Fundamental Computer Vision Problems, Both Low-Level (E G , Optical Flow Estimation) And High-Level (E G , Object Detection And Semantic Segmentation), Synthetic Environments And Datasets For Outdoor And Urban Scenes (Autonomous Driving), Indoor Scenes (Indoor Navigation), Aerial Navigation, And Simulation Environments For Robotics Additionally, It Touches Upon Applications Of Synthetic Data Outside Computer Vision (In Neural Programming, Bioinformatics, Nlp, And More) It Also Surveys The Work On Improving Synthetic Data Development And Alternative Ways To Produce It Such As Gans The Book Introduces And Reviews Several Different Approaches To Synthetic Data In Various Domains Of Machine Learning, Most Notably The Following Fields: Domain Adaptation For Making Synthetic Data More Realistic Andor Adapting The Models To Be Trained On Synthetic Data And Differential Privacy For Generating Synthetic Data With Privacy Guarantees This Discussion Is Accompanied By An Introduction Into Generative Adversarial Networks (Gan) And An Introduction To Differential Privacy Binding:Hardcover, HardcoverPublisher:SpringerPublication Date:2021-07-13Weight:0.17 lbsDimensions:Number of Pages:346Language:English

Price: 174.51 USD

Location: USA

End Time: 2024-05-22T00:57:48.000Z

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