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 is the purpose of using updated weights in a neural network model?

• To initialize the model

• To improve the model's performance

• To reduce the model's complexity

• To increase the model's size

Which function is used to determine the test output in the neural network?

• ReLU

• Tanh

• Sigmoid

• Softmax

What does an accuracy of 62% indicate about the model's performance?

• The model is overfitting

• The model is performing adequately

• The model is not performing well

• The model is performing exceptionally well

What could be a reason for the low accuracy of the neural network?

• The model uses too many neurons

• The model has too many layers

• The dataset is too complex

• The dataset is too simple

What is suggested to potentially improve the neural network's performance?

• Adding more layers

• Decreasing the learning rate

• Using fewer neurons

• Reducing the dataset size