Deep Learning CNN Convolutional Neural Networks with Python - Practical Tips

The video provides practical tips for transfer learning, emphasizing the importance of following a complete machine learning pipeline. It discusses how the number of trainable layers in a pre-trained model should be adjusted based on the quantity of available data. For large datasets, training from scratch is possible but should start with pre-trained weights. The video concludes with a preview of a Python demo in the next session.

What is a crucial step in the machine learning pipeline when applying transfer learning?

• Ignoring overfitting issues

• Following the complete pipeline including data splitting

• Using only test data

• Skipping data validation

How should you handle a pre-trained model when you have a low quantity of data?

• Train all layers from scratch

• Freeze most layers and add a fully connected layer

• Unfreeze all layers

• Use random weight initialization

What is recommended when you have a medium level of data for transfer learning?

• Train the model from scratch

• Freeze all layers

• Unfreeze the last few layers

• Use random initialization for all layers

What should you do if you have a large amount of data for training a model?

• Freeze all layers

• Use random initialization for all layers

• Train the model from scratch without pre-trained weights

• Initialize with pre-trained weights and train all layers

Why is it recommended to initialize weights with pre-trained values even when training from scratch?

• It avoids the need for validation

• It reduces the model size

• It is faster to train

• It helps in better convergence

What is the relationship between data quantity and the number of trainable layers in transfer learning?

• More data allows more layers to be trainable

• Less data allows more layers to be trainable

• Data quantity does not affect trainable layers

• All layers should always be trainable

What is a practical tip for initializing weights in transfer learning?

• Do not initialize weights

• Use pre-trained weights for initialization

• Use zero weights for initialization

• Use random weights for all layers