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 a 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 May 2019, two engineers from Google brain team named Mingxing Tan and Quoc V. Le published a paper called “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks”. The core idea of publication was about strategically scaling deep neural networks but it also introduced a new family of neural nets, EfficientNets.

EfficientNets, as the name suggests are very much efficient computationally and also achieved state of art result on ImageNet dataset which is 84.4% top-1 accuracy.

So, in this article, we will discuss EfficientNets in detail but first, we will talk about the core idea introduced in the paper, model…

Designing a medical device that adds value to end user and simultaneously captures profitable market share is really a tough job.

Is it because healthcare is a life-critical segment? Or is it because it involves complex procedures?

Apparently, both. In addition, it needs to be aligned with healthcare regulatory requirements, solution specifications and should deliver functionalities to satisfy end user needs.

Hence, it seeks a holistic approach to design a medical device rather than being an isolated part of the complete process. …

Anand Borad

Learning the weights of life with ReLu. Taking positives and eliminating negatives.

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