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Deep Learning is still an extension of traditional neural network. It is type of machine learning technique that employs the deep neural network. Deep neural network is the multilayer neural network that contains two or more hidden layers. ConvNet is a technique that uses deep learning approach that is used for image recognition. ConvNet has conquered most computer vision techniques and is growing at a rapid place. ConvNet is deep neural network that has many hidden layers. It intimates how the visual cortex of human brain processes and recognizes images. Neural networks have a hard time understanding this concept on first encounter. ConvNet differs in concept and operation from previous neural networks. The deep neural network learning rule becomes the algorithm that generates the model from the training data. The deep neural network learning rule becomes the algorithm that generates the model from the training data. Deep Learning allows the development, training and use of neural networks. There are three classes of Artificial Neural Network that are Multilayer Perceptrons (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Networks (RNN). Multilayer Perceptrons are the classical type of neural network. It consists of one or more layers of neurons. Data is fed to the input layer and hidden layers providing levels of abstraction. Predictions are may made on the output layer. It is also known as visible layer. MLPs are widely used for classification problems where inputs are assigned a class. MLPs are also suitable for regression problems where a real valued quantity is predicted given a set of inputs. Data is provided in a tabular format and classification and regression prediction problems. MLPs are flexible in nature and generally used to learn a mapping from inputs to outputs. The pixels of an image or document can be rearranged to one long row of pixels in a length of data and fed into a MLP. MLPs can solve the problems of image data, Text data, Time series data and many more.