Price: $169.99 - $129.68
(as of Dec 26,2024 19:49:44 UTC – Details)

Publisher ‏ : ‎ Springer; 1st ed. 2021 edition (June 28, 2022)
Language ‏ : ‎ English
Paperback ‏ : ‎ 360 pages
ISBN-10 ‏ : ‎ 3030751805
ISBN-13 ‏ : ‎ 978-3030751807
Item Weight ‏ : ‎ 1.21 pounds
Dimensions ‏ : ‎ 6.1 x 0.82 x 9.25 inches

Synthetic Data for Deep Learning: A Game-Changer in Machine Learning

In the world of deep learning, one of the biggest challenges is the availability of large and diverse datasets for training models. This is where synthetic data comes in as a game-changer. In the book “Synthetic Data for Deep Learning” by Springer Optimization and Its Applications, experts explore the use of synthetic data to improve the performance of deep learning models.

Synthetic data refers to artificially generated data that closely mimics real-world data. By creating synthetic data, researchers can overcome the limitations of small and biased datasets, leading to more robust and accurate models. This book covers various techniques for generating synthetic data, including generative adversarial networks (GANs), data augmentation, and simulation.

With the rise of deep learning applications in fields such as healthcare, finance, and autonomous driving, the demand for high-quality training data is higher than ever. Synthetic data offers a cost-effective and scalable solution to this problem, enabling researchers to train models on diverse and realistic datasets.

If you’re interested in exploring the potential of synthetic data for deep learning, “Synthetic Data for Deep Learning” is a must-read. This book provides a comprehensive overview of the latest research and applications in this exciting field, making it essential reading for researchers, practitioners, and students in machine learning and artificial intelligence.
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