Deep Learning (DL) is a subset of Machine Learning (ML) that uses Artificial Neural Networks (ANNs) to learn patterns automatically. It powers complex tasks like image recognition, speech processing, and decision-making.
✅ Handles large datasets effectively
✅ Reduces manual feature engineering
✅ Scales to millions of parameters
✅ Enables complex applications (e.g., autonomous driving, medical diagnosis)
| Feature | Machine Learning (ML) | Deep Learning (DL) |
|---|---|---|
| Feature Engineering | Manual | Automatic |
| Small Data Performance | Good | Needs large data |
| Computational Power | Low (CPU) | High (GPU/TPU) |
| Interpretability | Easy to interpret | Acts as a "black box" |
| Training Time | Fast | Slower |
| Examples | SVM, Decision Trees, RF | ANN, CNN, RNN |
The simplest form of ANN introduced by Frank Rosenblatt (1958).
Mathematical form: