ICP9

Welcome to the In Class Programming 9:

Description: In this ICP we will be discussing Neural Network, Back-propagation, Activation Function, Linear Regression, Cost/Loss Functions, Gradient Descent (Optimization Algorithm) and Learning Rate.

Objective: To learn and implement artificial neural network techniques mentioned in the description. Then calculate and plot the loss and accuracy for the given data.

Implementation:

1.Plot the loss and accuracy for both training data and validation data using the history object in the source code.

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2.Plot one of the images in the test data, and then do inferencing to check what is the prediction of the model on that single image.

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3.We had used 2 hidden layers and Relu activation. Try to change the number of hidden layer and the activation to tanh or sigmoid and see what happens.

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4.Run the same code without scaling the images and check the performance?

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Video: ICP9

Conclusion: In this ICP we have learnt Neural Network, Back-propagation, Activation Function, Linear Regression, Cost/Loss Functions, Gradient Descent (Optimization Algorithm) and Learning Rate.