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English | 2022 | ISBN: 1633439615, 978-1633439610 | 177 pages | True PDF | 11.16 MB
Make your deep learning models more generalized and adaptable! These practical regularization techniques improve training efficiency and help avoid overfitting errors.
Regularization in Deep Learning teaches you how to improve your model performance with a toolbox of regularization techniques. It covers both well-established regularization methods and groundbreaking modern approaches. Each technique is introduced using graphics, illustrations, and step-by-step coding walkthroughs that make complex math easy to follow.
You'll learn how to augment your dataset with random noise, improve your model's architecture, and apply regularization in your optimization procedures. You'll soon be building focused deep learning models that avoid sprawling complexity and deliver more accurate results even with new or messy data sets.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
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