Deep learning is a form of machine learning that uses multilayer neural networks to learn complex patterns from data. Transfer learning is a training strategy that reuses knowledge from a pretrained model for a different but related task.
How Deep Learning Works
A deep learning model has an input layer, multiple hidden layers, and an output layer. Data passes forward to produce a prediction, and a loss function measures the difference between the predicted and expected outputs. Through backpropagation, an optimization algorithm adjusts the network's weights to reduce this loss. Earlier layers may detect simple features such as edges, while later layers combine them into shapes and complete objects.
How Transfer Learning Works
Transfer learning starts with weights learned from a large source dataset. These weights provide a starting point for the target task. Some layers may be frozen so their weights remain unchanged, while others are retrained or fine-tuned on a smaller target dataset. For example, a general image model could be adapted to classify plant diseases. This usually reduces target data needs, training time, and computational cost.
| Concept | Deep learning | Transfer learning |
|---|---|---|
| Main idea | Learns representations with a multilayer neural network | Reuses representations from a previous task |
| Data | Often needs a large dataset | Can work with less target-task data |
| Advantage | Models complex patterns automatically | Saves training time and computation |
| Limitation | Requires substantial data and processing power | Best when source and target tasks are related |
A common misconception is that transfer learning is a separate neural network type. It is instead a strategy often applied to deep learning models.
Exam technique: For an explain question, describe reusing pretrained weights and then fine-tuning selected layers. For distinguish, state that deep learning describes the model architecture or approach, whereas transfer learning describes how existing learned knowledge is reused.
