JOURNAL OF CRITICAL REVIEWS ISSN- 2394-5125 VOL 7, ISSUE 05, 2020 An Epitome of Deep Learning algorithms, Frame works and its Applications G.Nirmala, Dr.K.K.Thygharajan Received: 28 February 2020 Revised and Accepted: 06 March 2020 Abstract: Artificial Intelligence is a super set of current technology comprising of machine learning in turn deep learning. A detailed analysis and assessment of deep learning framework and various application areas that is currently available with its strength and weakness was depicted . Deep learning is now days preferred by everyone due to its data science and computer vision capabilities. All these frameworks are free of cost and available as open source. In this paper the major areas of applications are elaborately discussed. A snapshot on various algorithms that are frequently used in deep learning is depicted. Deep learning frame works which are familiar are tabulated with its important characteristics. Keywords: Deeplearning, Machinelearning, CNNs,Frameworks. 1. Introduction Deep learning is a current topic and need of the hour which has effective, supervised,time and cost efficient methods to be used in wide range of applications such as business,social media,science and government. Machine learning makes the process of feature extraction, analysis and understanding of useful information from digital images very easy. Computer vision includes tasks like object recognition, classification, object detection and image segmentation.. It has been a great challenge to define recursive neural network, recurrent neural network,convolution neural network and unsupervised pre trained neural network. It plays a vital role in data science in the area of biological and medical science where there are oceans of data for analysis. Deep learning is a subset of machine learning. This review provides research directions to the fellow researchers who are interested to work in this field. This paper provides an insight into various algorithms used in deep learning and the fields where it is applied. [1] jaison bunk and etal detected a tampered region forgery using LSTMS to classify the location. Deep learning is the need of the hour. Athanasios Voulodimos [2] and etal written a complete review on deep learning methods and developements in computer vision prespective. 2. Panaroma on Deep Learning Algorithms: CNNs: Convolution Neural Network (CNN) was proposed in 1980 where convolution layer is the major building block in CNN of from the research of synchronous oscillation phenomenon in visual cortical neurons of cat in the case of external pulsing. even though it needs a tough job in estimating the optimum values for the large number of CNN parameters and finding out the optimal number of features to be generated, CNN has been widely used by researchers in image processing because the CNN does not require any training and it can produce minimal number of features even for large images. The feature extraction based on CNN was used by many researchers to distinguish the regions of images which are invariant to scale, rotation [8].CNNs uses two type of approaches. using Machine learning 1343 JOURNAL OF CRITICAL REVIEWS ISSN- 2394-5125 VOL 7, ISSUE 05, 2020 algorithms for feature and diamensionality reduction like eigen values,Singlevaluedecomposition,PCA, Random forest, High correlation etc.., and classicalmethods.classical methods inturn has three main layers Convolution layer which uses filters to select edges, lines and various features from the given input image.these filters are called weights or parameters gives an array of numbers as output which is called feature map or activation map.second layer call ReLu which converts normalises the feature map by dividing with total number of pixels.It also converts negative value to 0 and makes the activation map all positve values . The function of the last layer called Maxpooling or average pooling layer is it takes input from feature map and find the average or maximum fetaure etection with minimum diamension.Asifullah Khan [4] etal presented CNN has automatic feature extraction capability. RNNS:Recurrent neural networks is a classification of artificial neural networks which is used for text data in which the association between the nodes forms a directed graph with a temporal sequence by applying recursive set of weights. It takes the input as sequence and gives output also as a sequence. It is mainly used for implementing natural language processing and language translations.It takes the input at each step and also previous step ouput like feedbackwith a small memory.Wenguan wang [5] and etal proved a novel deep learing based salient object detection algorithm with cross data sets and evaluation results. Yan lechun and etal [6] reviewed and given a clear insight of deep learing algorithms . LSTMS: Long Short Term Memory Networks abbreviated as LSTMs introduced in 1991 by Hochreiter [16] was more innovative, efficient and gradient based method. It is local in space and time and its time complexity is 0(1).It has three model of converting from vector to sequence model where the input is a image as input and output is numeric array as sequence in image captioning,changing from sequence to vector model sentiment analysis where input is a word and output is vector.third model is sequence to sequence in which both input and output is word used in natural language processing.It has a cell state factor representing from ct-1 to ct.It comprises of three gates input gate, forget gate and output gate. Finally distributes the gradient.It is much complicated . It is changed to advanced version with only two gate reset gate and update gate called as GRU gated recurrent unit. AutoEncoders: Auto encoders are conversion algorithms which compress the given data input sequence to another sequence again revert back to the original given input as ouput. it encodes the given text to encrypted format and again decrypts or decodes back to the input.the intermediate represenation with minimal code is called latent-space representation. Aditya Khamparia [7] briefly in his survey explained the processing of auto encoders for better results the number of hidden layers and introduced with sparsity constraints. Shetra [8] and etal tabulated implemeted models of autoencoders are two types variational and sparse samples commonly applied for diamentionality reduction and encoding in unsupervised learning. DBN: In Deep Belief Networks every two consecutive layers form a RBM Restricted Boltzman Machine.Ghasemi[20] proposed DBF are more popular and giving more accuracy like deep neural networks.It can be 1344 JOURNAL OF CRITICAL REVIEWS ISSN- 2394-5125 VOL 7, ISSUE 05, 2020 trained in a greedy and a pictorial representation for extracting deep represenational data.It has layer wise pretraing as first step and then final tuning stage. 3. Frameworks: Deep Learning frame works plays a main role in solving business challenges. AI rightly partnered with deep learning framework completed the task very effectively and efficiently.researchers according to the requirements can choose the type of particlular frameworks.all these frameworks are free of cost and available as open source to cater the faster need of wide range of developers and reasearchers.some of the software frame works for deep learning are Tensorflow,keras,cafee,pytorch,microsoft cognitive toolkit.MXNet,DL4j are to be discussed in table1 here. Name of Speciality Multiple the Frame GPU Languages Developer Deep work Support Learning Architecture Supported Tensorflow Python Google CNN,RNN, Multiple level Yes of Abstraction C++ LSTMs... in ML pipeline keras Python Google CNN,RNN Dropout,batch GPUS and normalization, TPUs Network pooling library caffe C++ Berleley CNN Speed, CPU to AI GPU with python robustness, Researrch Interface Moderation modularity pytorch Python Facebook CNN,RNN Numpy, yes AI C++ Automated Research Differentiation cuda lab and Microsoft microsoft C++ Microsoft yes Distributed Multiple cognitive Research deep learning GPUS and toolkit servers Stochastic gradient decent MXNet C++,Matlab,R, Apache CNN,RNN, Cloud, No Python,Julia, LSTMs... faster scala, Learning rate 1345 JOURNAL OF CRITICAL REVIEWS ISSN- 2394-5125 VOL 7, ISSUE 05, 2020 DL4j Java,Scala Apache RNN,LSTMs, Effective Supports library for Multiple DL4j Cuda RBM,DBM Linear GPUS python Algebra an d Matrix Manupulations contributions in github contributions as on 10/5/2020 Tensor keras caffe pytorch microsoft MXNet DL4j cognitive flow toolkit 2485 816 265 1395 199 792 37 Deployment areas of Deeplearning: Self-Driven cars: Deep learning algorithm RNNis used to train the automated human less cars by many trials and Convulsion neuralnetwork is applied for objcet identification.Train an end to end deep learning model for an automated car with a driving simulator is sucessfully launced. Game Playing:Deep learning algorithms have been applied in video games like arcade games,firts-person shooters.Deep Q-learning algorithm for playing snamk game and deep reinforement leaarning algorithm using keras and tensorflow for handling extreme large decision spaces and sparse rewards. Health care: Health care industry cover many hidden openings and similarites in testing clinical data which inturn helps doctors to treat thier patients effectively.There are three important applications in Health care. First one is discovery of medicines and drugs.Deeplearning is very much useful in the discovery of medicines and thier progress 1346 JOURNAL OF CRITICAL REVIEWS ISSN- 2394-5125