Types of Regularization Techniques To Avoid Overfitting In Learning Models

Regularization Techniques

Regularization is a set of techniques which can help avoid overfitting in neural networks, thereby improving the accuracy of deep learning models when it is fed entirely new data from the problem domain. There are various regularization techniques, some of the most popular ones are — L1, L2, dropout, early stopping, and data augmentation. Why […]

How Data Augmentation Impacts Performance Of Image Classification, With Codes

image data augmentation

The article demonstrates how to do data augmentation to increase the size of the data. We will first build a deep learning model without performing augmentation and will compute the accuracy. After which we will build a similar deep learning model after performing augmentation and compute the accuracy. Finally, we will compare the performance of both models.