Deep Learning - Deep Neural Network for Beginners Using Python - Testing (NN Implementation)

The video tutorial discusses the process of testing a neural network model using updated weights. It explains how to calculate the model's accuracy and highlights the importance of analyzing performance to identify areas for improvement. The tutorial suggests that using more neurons and layers could enhance the model's performance, especially when dealing with complex datasets. The video concludes with a brief mention of upcoming lectures on optimization techniques.

What are the updated weights used for in the model testing process?

How is the accuracy of the model calculated after testing?

What does a 62% accuracy on a testing set indicate about the model's performance?

Why might a neural network with a low accuracy need more neurons or layers?

What factors can affect the performance of a neural network?