Explore the key characteristics of shallow neural networks, including their single hidden layer structure, and how they differ from deep neural networks in complexity and applications.
Table of Contents
Question
Which neural network has only one hidden layer between the input and output?
A. Shallow neural network
B. Deep neural network
C. Feed-forward neural networks
D. Recurrent neural networks
Answer
A. Shallow neural network
Explanation
Shallow neural network: The Shallow neural network has only one hidden layer between the input and output.
Understand Shallow Neural Networks
A shallow neural network is characterized by having only one hidden layer between the input and output layers. This simple architecture distinguishes it from more complex deep neural networks, which contain multiple hidden layers.
Key Features of Shallow Neural Networks
Structure: A shallow neural network typically consists of three main components:
- Input layer: Receives the raw data
- Hidden layer: A single layer where computation and feature extraction occur
- Output layer: Produces the final prediction or output
Complexity: Due to their simpler architecture, shallow neural networks have lower complexity compared to deep neural networks. This makes them easier to train and less prone to issues like vanishing gradients.
Learning Capacity: Shallow neural networks have a limited learning capacity, making them suitable for tasks where relationships in the data are relatively simple or linear.
Applications and Limitations
Shallow neural networks are often used for:
- Simple classification tasks
- Basic regression problems
- Scenarios with limited data or computational resources
However, they may struggle with complex tasks that require understanding intricate patterns or hierarchical representations of data, which is where deep neural networks excel.
In conclusion, while shallow neural networks have their place in certain applications, the field of neural networks has largely moved towards deeper architectures to tackle more complex problems in areas such as image recognition, natural language processing, and advanced predictive analytics.
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