Create a computer vision system using decision tree algorithms to solve a real-world problem : Code to Train a perceptro

The video tutorial covers training a simple perception network, starting with data loading and network building. It explains model compilation using binary cross entropy and the Adam optimizer, followed by training and evaluation. The tutorial concludes with visualization of results and a discussion on weights and biases.

How many epochs were used during the training of the network?

What is the purpose of the confusion matrix in assessing the model?

What does the term 'meshgrid' refer to in the context of visualizing results?

What is the significance of the weights and biases in a neural network?