Data Science and Machine Learning (Theory and Projects) A to Z - Transfer Learning: Why Transfer Learning

The video tutorial explains transfer learning, a technique where a pre-trained model is adapted for a new task by trimming and adding layers. It highlights the importance of data similarity for effective transfer learning and discusses how neural networks learn features at different layers. The tutorial emphasizes the practical application of transfer learning in computer vision, particularly using models trained on the diverse Imagenet dataset.

What is the primary goal of transfer learning?

• To use a pre-trained model and adapt it to new data

• To create a new model from scratch

• To reduce the size of the dataset

• To increase the number of layers in a model

Under what condition is transfer learning most effective?

• When the new data is completely different from the pre-trained model's data

• When the pre-trained model is trained on a small dataset

• When the pre-trained model has fewer layers

• When the new data is similar to the pre-trained model's data

What type of features do the early layers of a convolutional neural network learn?

• Features related to texture

• Features related to color

• Generic features like edges and corners

• Class-specific features

How do deeper layers in a convolutional neural network differ from the early layers?

• They learn more generic features

• They learn more class-specific features

• They do not learn any new features

• They learn features unrelated to the input data

What is the Imagenet dataset known for?

• Containing a small number of categories

• Being unsuitable for transfer learning

• Being used for training models in natural language processing

• Having a large variety of diverse image categories

Why is transfer learning particularly useful in computer vision tasks?

• Because it eliminates the need for any training

• Because it requires a large amount of new data

• Because it allows the use of pre-trained models on diverse datasets

• Because it only works with text data

What is a key benefit of using transfer learning with models trained on Imagenet?

• It requires no data for training

• It is only applicable to non-visual data

• It allows for the transfer of class-specific features

• It provides a foundation of generic features that can be adapted