Need of Transfer Learning in Deep Learning

Transfer learning is particularly useful in the following scenarios:

• Limited Data: When the dataset is not large enough to train a deep neural network from scratch.
• Time and Resource Efficiency: Training deep networks from scratch is computationally expensive and time-consuming.
• Improved Performance: Pre-trained models often lead to better performance as they start with learned features rather than random initialization.

Transfer learning is a powerful technique used in deep learning tasks. Here, a model developed for a particular task is reused as a starting point for a model on the second task. Thus, transfer learning uses the knowledge gained from a pre-trained model and allows faster convergence with better performance, especially when the data is limited.

On the other hand, fine-tuning takes this concept further by adjusting the parameters of the pre-trained model so that it will fit better to the new task. Both of these techniques are particularly useful when you need to train deep neural networks that are data and compute-intensive.

This article will explore how to implement transfer learning and fine-tuning using Keras, demonstrated with the CIFAR-10 dataset and the VGG16 model.