Hidden layers
Hidden layers are the layers between the input and output layers in a neural network. They are responsible for learning the representational "features" of the data.
Types of Hidden Layers
1. Dense (Fully Connected) Layers
Every neuron in the layer is connected to every neuron in the previous layer.
Best for: Tabular data and final classification/regression heads.
Complexity: $O(n \times m)$ weights, where $n$ and $m$ are the layer sizes.
2. Convolutional Layers (Conv)
Applies a set of learnable filters to the input to extract spatial features (edges, textures, shapes).
Key Concepts: Kernels, Stride, Padding, Dilated Convolutions.
Best for: Images and spatial data.
3. Recurrent Layers (RNN/LSTM/GRU)
Maintains a "hidden state" that captures information from previous steps in a sequence.
Best for: Sequential data (text, time-series, audio).
4. Attention Layers
Allows the model to focus on specific parts of the input sequence regardless of their distance.
Mechanism: Query, Key, Value vectors.
Best for: Long-range dependencies in NLP (Transformers).
Intuition: Hierarchical Representation
The beauty of deep learning lies in how hidden layers decompose complex problems:
Early Layers: Learn simple features (e.g., edges, blobs).
Mid Layers: Combine simple features into parts (e.g., eyes, wheels).
Late Layers: Combine parts into objects (e.g., faces, cars).
Interview Questions
1. "What happens if you have no hidden layers?"
The model becomes a simple linear classifier (like Logistic Regression) and cannot learn non-linear decision boundaries.
2. "How do you choose the number of hidden layers and neurons?"
Generally via hyperparameter tuning. However, the modern trend is to use established architectures (e.g., ResNet, BERT) and fine-tune them rather than designing from scratch.
3. "Explain the role of Activation Functions in hidden layers."
They introduce non-linearity. Without them, the entire network would collapse into a single linear transformation, regardless of the number of layers.
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