Objective: In this module, you will get a basic understanding of deep learning and what kind of problems deep learning will address. Also, get clarity about the difference between Machine learning and Deep learning.

Objective: In this module, you will get an idea about the deep learning structure and how we can build it using Neurons (Perceptrons). You will familiarize yourself with different activation functions and have a brief introduction to the TensorFlow framework.

Objective: In this module, you will be learning about Deep Neural Networks and how does it work. Also, you will get more understanding of forwarding and backward propagation.

Objective: In this module, you will get more hands-on for the TensorFlow framework. You will get to know more details about the data flow in TensorFlow.

Objective: In this module, you will be learning what a convolutional neural network is and how it is different from Feedforward neural network. Also, you will get an understanding of various layers in the Convolutional Neural Network and what are the real-time applications of CNN.

Objective: In this module, you will get an understanding of what is RNN and it’s working. Also, get to know the advantage of RNN over other Neural Network models and you will be familiarised with LSTM, why we need to use LSTM, and the real-time application of LSTM.

Objective: In this module, you will get a great understanding of RBM and Autoencoders. Also, you will get an idea about how Autoencoders are different from PCA and you will be gone through some real-time applications of RBM and Autoencoders.

Objective: In this module, you will get to know the various functions and features of Keras API and how does it use. After this session, you will be able to develop a Neural Network model using Keras API.

Objective: In this module, you will get to know the different functionalities and features of TFLearn API and how does it use. After this session, you will be able to develop a Neural Network model using TFLearn API.