Deep Learning - Artificial Neural Networks with Tensorflow - ANN for Regression

The video tutorial demonstrates using synthetic data for neural network regression. It covers creating a dataset with a nonlinear function, building a neural network model, and training it with a custom learning rate. The tutorial emphasizes the importance of visualizing data and predictions, and it explores the model's limitations in extrapolating periodic functions.

What role does the cosine function play in the dataset creation?

Describe the architecture of the neural network used in the example.

How can one visualize the prediction surface of a neural network?

What challenges might arise when trying to extrapolate using a neural network?