A deep neural network (DNN) is essentially a neural network with multiple hidden layers, whereas a "neural network" typically refers to a network with just one hidden layer. The "deep" in deep neural network signifies the depth of the network, referring to the number of layers.
Here's a breakdown:
-
Neural Network (Shallow Neural Network):
- Contains one input layer, one hidden layer, and one output layer.
- Each node in one layer is connected to every node in the adjacent layer (fully connected).
- Suitable for simpler problems where complex feature extraction isn't necessary.
-
Deep Neural Network (DNN):
- Contains one input layer, multiple hidden layers (more than one), and one output layer.
- Each layer learns a hierarchy of features, with earlier layers learning simpler features and later layers learning more complex ones.
- Better equipped to handle complex problems with high-dimensional data due to its ability to learn intricate patterns. This is because the multiple layers allow for more sophisticated feature engineering directly within the network.
- Examples include Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
- DNNs are a subset of Machine Learning and fall under the umbrella of Deep Learning.
Here's a table summarizing the key differences:
| Feature | Neural Network (Shallow) | Deep Neural Network (DNN) |
|---|---|---|
| Hidden Layers | One | Multiple (two or more) |
| Complexity | Lower | Higher |
| Feature Extraction | Limited | Automatic, Hierarchical |
| Problem Suitability | Simpler problems | Complex problems |
| Learning | Less complex patterns | Intricate patterns |
| Examples | Logistic Regression (as a simple neural network) | CNNs, RNNs, Transformers |
In essence, the "deep" in deep neural networks refers to the presence of multiple hidden layers, enabling these networks to learn more complex representations of data compared to traditional, shallow neural networks. This allows DNNs to tackle significantly more challenging tasks.
