Deep Learning Concepts and Libraries Used in Image Analysis and Classification

Abstract

Deep learning has gained much importance in today’s digital world. There are many algorithms in the industry which talk about how Convolutional neural networks or deep learning algorithms can be used to parse, analyze and predict type of an image and on how to extract image features or properties. Sometimes if the algorithm is not properly written and maintained then we may get wrong predictions and incorrect output results. It depends on the type of the data used in training the model.

To avoid such misconceptions, we here describe the hyper parameters and concepts that will help in writing better and optimized algorithm. Parameters like strides, epochs, pooling or number of layers are explained with examples. These parameters play key role in feature extraction. Here we also explain about the different technologies available in the industry till 2018, to write a deep neural network code along with different python libraries that can be used in image processing. Any researcher who want to do a statistical analysis in deep learning need to undergo various metrics in understanding the concepts. To make the task of researchers much easy we have grouped various Deep learning concepts and libraries as a study.