Loss functions for Regression algorithms

A Complete Guide to Loss functions for Regression Algorithms

Loss function in supervised machine learning is like a compass that gives algorithms a sense of direction while learning parameters or weights. This blog will explain the What? Why? How? and When? to use Loss Functions including the mathematical intuition behind that. So without wasting further time, Let’s dive into the concepts. What is Loss … A Complete Guide to Loss functions for Regression Algorithms Read More » ...
Image Data Augmentation

Guide to Image Data Augmentation: from Beginners to Advanced [tensorflow + Keras]

Image augmentation is an engineered solution to create a new set of images by applying standard image processing methods to existing images.

This solution is mostly useful for neural networks or CNN when the training dataset size is small. Although, Image augmentation is also used with a large dataset as regularization technique to build a generalized or robust model.

Deep learning algorithms are not powerful just because of their ability to mimic the human brain. They are also powerful because of their ability to thrive with more data. In fact, they require a significant amount of data to deliver considerable performance.

High performance with a small dataset is unlikely. Here, an image data augmentation technique comes in handy when we have small image data to train an algorithm. ...