Introduction to Neural Networks and Deep Learning

This module will help you understand artificial neural network better with the help of the diagram of the Bias Layer. You will learn about a fully connected artificial neural network and the layers involved in it and go through the mathematical foundations for artificial neural networks. 
 

In order to enhance your understanding of the mechanism of Deep Learning or a neural net model, this module puts forth a TensorFlow Playground demo.

This module will walk you through Deep Learning concepts like artificial neural networks, activation functions, back propagation, and feed forward nets.
 

This module will let you comprehend the role of the basic set of layers in Deep Learning. You will go through Dense Layer, Dropout Layer, Convolution 1D, Convolution 2D, MaxPooling 1D, and LSTM in detail.
 

This module digs deeper into the activation function and elaborates on linear and non-linear methods of using activation functions.
 

This module explains Deep Learning with Keras demonstration to help you understand working with industry-oriented problems. 

This module first briefs CNN's history and then continues with explaining its features. It then explains the structure of CNN and defines its terms. 

This module starts by introducing you to Recurrent Networks. You will learn about feed forward networks and recurrency. You will also go through RNN and LSTM diagrammatic representation with a thorough explanation. Lastly, you will comprehend long short-term memory.
 

This module brings different types of chatbots to your knowledge with sample case studies and discusses conventional interface concept. 

This module demonstrates CNN and LSTM with Keras and Tensorflow to help you understand working with industry-oriented applications.