Knowledge Transfer, often referred to as Transfer Learning in the context of deep learning, is a machine learning paradigm where a model trained on one task is re-purposed or adapted for a second, related task. The core idea is to leverage the features, patterns, or knowledge learned from a large dataset or a complex task and apply it to a new task where data might be scarce or the training process would be computationally expensive from scratch. In deep learning, this commonly involves taking a pre-trained neural network (e.g., a CNN trained on ImageNet) and using its learned weights as an initial state for a new model, often by freezing some initial layers and fine-tuning the later layers or adding new layers. This approach significantly reduces training time, improves performance, and addresses data scarcity issues, making it a highly effective technique across various machine learning applications, especially in computer vision and natural language processing.