Transfer Learning is an adaptive technique where knowledge gained from one task is utilized in a related, but distinct, target task. In contrast to traditional ML techniques, which are typically task-specific and learn from scratch, transfer learning expedite learning based on an existing, complementary task.

Key vs. target task distinction

Traditional ML: The algorithm starts with no information about the task at hand and learns from the provided labeled data.

Transfer Learning: The model uses insights from both the key and the target task, preventing overfitting and allowing for improved generalization.

Data requisites for each approach

Traditional ML: Essentially, a large and diverse dataset that’s labeled and completely representative of the task you want the model to learn.

Transfer Learning: This approach can operate under varying degrees of data constraints. For instance, you might only need limited labeled data from the target domain.

Training methods

Traditional ML: The model is initially provided with random parameter values, and learns to predict off the examined data through techniques like stochastic gradient descent.

Transfer Learning: The model typically begins with parameters that are generally useful or beneficial from the key task. These parameters are further fine-tuned with data from the target task and can also be frozen to restrict further modifications based on the key task.

Fitness for different use-cases

Traditional ML: Perfect for tasks where extensive labeled data from the target task is accessible and is highly typical.

Transfer Learning: Exceptional for situations with lesser labeled data, or when knowledge from the key task can significantly enhance learning on the target task.