Deep Learning via Multilayer Perceptron Classifier - DZone Big Data

Deep learning which is currently a hot topic in the academia and industries tends to work better with deeper architectures and large networks. The application of deep learning in many computationally intensive problems is getting a lot of attention and a wide adoption. For example, computer vision, object recognition, image segmentation, and even machine learning classification. Some practitioners also refer to Deep learning as Deep Neural Networks (DNN), whereas a DNN is an Artificial Neural Network (ANN) with multiple hidden layers of units between the input and output layers. Similar to shallow ANNs, DNNs can model complex non-linear relationships [1]. The DNN architectures for example for object detection and parsing, generates compositional models where the object is expressed as a layered composition of image primitives. The extra layers enable composition of features from lower layers, giving the potential of modeling complex data with fewer units than a similarly performing shallow network.