Describe the concept of transfer learning in neural networks and explain how pre-trained models can be used to solve new tasks.

Transfer learning is a concept in neural networks that allows the knowledge gained from solving one task to be transferred and applied to solve a different but related task. It leverages the idea that features learned by a model while training on one task can be useful for learning another task. In transfer learning, a pre-trained model, which has been trained on a large dataset, is used as a starting point for a new task, instead of training a model from scratch. The process of transfer learning involves the following steps: 1. Pre-training: A deep neural network model is trained on a large dataset for a specific task. This pre-training step involves several layers and iterations to learn the patterns and representations in the data. 2. Feature Extraction: The pre-trained model is used as a feature extractor. The input data for the new task is passed through the pre-trained layers, and the output of these layers (features) is extracted. These features capture the high-level representations learned by the pre-trained model. 3. Fine-tuning: After feature extraction, the extracted features are fed into a new set of layers that are specific to the new task. These layers are typically initialized randomly and are trained using a smaller ....