Create a machine learning model of a real-life process or object : Improving the Network with Better Activation Function

The video tutorial discusses improvements in neural networks by using better activation functions and dropout techniques. It explains the use of rectified linear units and dropout to prevent overfitting in a multilayer perceptron model. The tutorial also covers the change from stochastic gradient descent to the Adam optimizer for better performance. The results show a significant improvement in mean absolute error, and further suggestions are made to enhance the model's performance by adjusting the number of epochs.

What is the default activation function for the dense layer in the multilayer perceptron?

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How does the rectified linear unit (ReLU) activation function behave for negative and positive inputs?

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What effect does dropout have on the neural network's performance?

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Why is it important to prevent overfitting in a neural network?

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What optimizer was changed to Adam, and why is it considered more advanced?

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What were the results on the train set after implementing the changes?

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What is suggested as an exercise regarding the number of epochs?

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