Synthetic Data for Deep Learning

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Synthetic Data for Deep Learning: a book by Sergey Nikolenko

$ 170

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This is the first book on synthetic data for deep learning, and its extensive coverage could make this book the standard benchmark for synthetic data for years to come. The book can also serve as an introduction to some other important areas of machine learning that are seldom covered in other books.

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360 pages, published in 2021

Synthetic Data for Deep Learning book PDF free download

Machine learning as a discipline would not be possible without the inner workings of optimization. The book contains the nerves necessary for optimization, whereby the core of the discussion lies around the increasingly popular tool for training deep learning models - synthetic data. In the field of synthetic data, exponential growth is expected in the near future. This book is a comprehensive study of the field.

At its simplest, synthetic data refers to computer-generated graphics that are used to train computer vision models. There are many other facets of synthetic data that need to be considered. In the Basic Computer Vision section, the book covers basic computer vision problems, both low-level (e.g., optical flow estimation) and high-level (e.g., detection objects and semantic segmentation), synthetic environments and data sets for Exterior and city scenes. (autonomous driving), indoor scenes (indoor navigation), flight navigation and simulation environments for robotics. In addition, it deals with synthetic data applications outside of computer vision (in neural programming, bioinformatics, NLP, etc.). It is also reviewing work to improve the development of synthetic data and other methods of producing it, such as: B. GANs.

The book presents and reviews different approaches to synthetic data in different areas of machine learning, including the following areas: domain matching to make synthetic data more realistic and / or adapting models to be trained on synthetic data, and differential confidentiality to make synthetic Generate data. Data with confidentiality guarantees. This discussion is accompanied by an introduction to Generative Adversarial Networks (GANs) and an introduction to differential confidentiality.

Synthetic Data for Deep Learning Book

This is the first book on synthetic data for deep learning, and its broad coverage could make this book the standard reference for synthetic data for years to come. The book can also serve as an introduction to other important areas of machine learning that are rarely covered in other books. Machine learning as a discipline wouldn't be possible without the inner workings of optimization. The book contains the necessary optimization elements, although the core of the discussion is the increasingly popular tool for training deep learning models, namely synthetic data. The field of synthetic data is expected to grow exponentially in the near future. This book is an overview of the region.

In its simplest form, synthetic data refers to computer-generated graphics that are used to train computer vision models. There are many other facets of synthetic data that must be taken into account. In the section on basic computer vision, the book deals with basic problems of computer vision, both low level (e.g. estimation of optical flow) and high level (e.g. object recognition and semantic segmentation), synthetic environments and data sets both external and external city scenes (autonomous driving), indoor scenes (indoor navigation), air navigation and simulation environments for robotics . In addition, he discusses synthetic data applications outside of computer vision (in neural programming, bioinformatics, NLP and more). It also examines work aimed at improving the development of synthetic data and alternative means of creating it, such as: B. GANs.

The book presents and discusses different approaches to synthetic data in different areas of machine learning, especially in the following areas: Domain adaptation for more realistic design of synthetic data and / or adaptation of models to train on data synthetic and differential confidentiality for the generation of synthetic data Data protection guarantees. This discussion is accompanied by an introduction to General Adversarial Networks (GANs) and an introduction to Differential Confidentiality.

About the Author

Sergey I. Nikolenko is a computer scientist specializing in machine learning and algorithm analysis. He is Head of AI at Synthesis AI, a San Francisco-based company specializing in the generation and use of synthetic data for modern machine learning models, and is also Head of the Artificial Intelligence Lab at Steklov Mathematical. Institute of St. Petersburg. Russia. Dr. Nikolenko's interests include synthetic data in machine learning, deep learning models for natural language processing, image manipulation and computer vision, and algorithms for networking. His previous research includes work in cryptography, theoretical computer science, and algebra.

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