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.

What is the primary purpose of the sigmoid activation function in a neural network?

• To introduce non-linearity into the model

• To ensure the model is linear

• To increase the number of neurons

• To decrease the model's complexity

Why is binary cross-entropy used as the loss function in this model?

• To simplify the model's architecture

• To increase the model's complexity

• Because the model has a single binary output

• Because the model has multiple outputs

What is the role of the Adam optimizer in training the neural network?

• To optimize the learning rate

• To compile the model

• To initialize the weights

• To update weights using backpropagation

Why is it important to divide data into training and testing sets?

• To ensure the model is overfitted

• To validate the model's performance on unseen data

• To reduce the number of epochs

• To increase the training time

What does a confusion matrix help to determine in model evaluation?

• The complexity of the model

• The number of neurons in each layer

• The number of layers in the model

• The accuracy of the model's predictions

What is the purpose of using a meshgrid in visualizing neural network results?

• To increase the number of neurons

• To visualize decision boundaries between classes

• To simplify the model architecture

• To reduce the training time

How does the model determine the decision boundary between two classes?

• By using a linear equation

• By increasing the number of epochs

• By reducing the learning rate

• By adjusting weights and biases

What does the adjustment of weights and biases in a trained network indicate?

• The model is overfitted

• The model has learned from the data

• The model has not learned anything

• The model is underfitted

What is the significance of the bias in a neural network?

• To decrease the model's complexity

• To shift the activation function

• To increase the number of neurons

• To ensure the model is linear

What is the main takeaway from this simple neural network example?

• Less data leads to better training

• More neurons lead to better accuracy

• Simple models can effectively classify data

• Complex models are always better