Ebook: Regularization in Deep Learning (MEAP v6)

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.

One of the most important goals in building machine learning and especially Deep Learning models is to achieve good generalization performance in the test dataset. The training task is considered to be completed when we have obtained a generalizable model, often with the help of proper regularization in the training process. While the theory of generalization still remains a mystery, it is an active research area with new insights being proposed.

Currently, there are quite a number of regularization techniques that have proved to be empirically effective in a specific training context. However, these resources are often scrambled and disconnected. This book intends to bridge the gap by offering a systematic and well-illustrated perspective on different regularization techniques, covering data, model, cost function, and optimization procedure. It even goes one step further by mixing the most recent research breakthroughs with practical coding examples on regularization in Deep Learning models.

This book entertains this complex and ever-growing topic in a unique way. It introduces minimal mathematics and technical concepts in a well-illustrated manner and provides practical examples and code walkthroughs offered via a step-by-step fashion. The teaching is designed to be intuitive, natural, and progressive, instead of forcing in a particular concept.

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.

about the reader
For data scientists, Machine Learning engineers, and researchers with basic model development experience.