Introduction to Neural Networks
What is Neural Network? Introduction to Neuron and Perceptron along with Primitive Neuron and Sigmoid Neuron. This course module will also explain the types of Activation functions that are used in deep learning networks. Gain an in-depth understanding of Cost Functions, Gradient Descent, Stochastic Gradient Descent, the feedforward model of the neural network, and the disadvantages of the feedforward model. Where applying weights to the feedforward model leads to and backpropagation algorithm.
Artificial Neural Network
This module will deal with the core understanding of Neural Networks, biological inspiration, perceptron learning, and binary classification, along with backpropagation Learning and Object Recognition.
Tensorflow & Keras
The Tensorflow and Keras module gives students exposure to Tensorflow, Debugging, and Monitoring, Keras for classification and regression in Typical Data Science Problems, and Setting up KERAS. Learn about the different layers in KERAS and how to create a Neural Network. Gain valuable information on the training models and monitoring along with Artificial Neural Networks.