• Mrs. Deepa K Bogle
• Dr.Parag U. Bhalchandra
• Dr.Aniket A. Muley

Abstract : Deep learning (DL) is an area of Artificial Intelligence which has been a part of digitalization solutions that has a major attention in the digital field. This review paper provides a brief overview of deep learning algorithms which are the most popular like, Auto-Encoders, Convolutional Neural Network (CNN), Deep Belief Networks (DBN), Long Short-Term Memory Networks (LSTMs), Restricted Boltzmann Machine (RBM) and Recurrent Neural Networks (RNNs), A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes and the challenges involved within.

AutoEncoders, Convolutional Neural Network, Deep Belief Networks, Long Short-Term Memory Networks, Restricted Boltzmann Machine and Recurrent Neural Networks.

"GLANCE ON VARIOUS DEEP LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 11, page no.734-739, November-2020, Available :http://www.jetir.org/papers/JETIR2011100.pdf

"GLANCE ON VARIOUS DEEP LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 11, page no. pp734-739, November-2020, Available at : http://www.jetir.org/papers/JETIR2011100.pdf