What Hidden Layer Meaning, Applications & Example
Internal layer in neural networks between input and output.
What is Hidden Layer?
A Hidden Layer is a layer in a neural network that lies between the input layer and the output layer. It is responsible for learning the complex patterns in the data. The nodes in this layer transform the inputs received from the previous layer using activation functions to produce outputs that are passed to the next layer.
Types of Hidden Layers
- Fully Connected Layer: Each neuron is connected to every neuron in the previous and next layers.
- Convolutional Layer: A type of hidden layer used in convolutional neural networks (CNNs) for feature extraction from images.
- Recurrent Layer: Found in recurrent neural networks (RNNs) to process sequential data by maintaining a memory of past inputs.
Applications of Hidden Layer
- Image Classification: Helps extract high-level features from raw image pixels.
- Speech Recognition: Processes sequences of audio features to recognize speech patterns.
- Time Series Prediction: Learns patterns from time-ordered data to make future predictions.
Example of Hidden Layer
In a neural network designed for image classification , the hidden layers might learn to detect features like edges, shapes, or textures in an image. These features are then used by the output layer to classify the image into categories like “cat” or “dog”.