Convolutional Neural Network (CNN)
A neural network architecture designed for processing grid-like data such as images.
In-depth explanation
CNNs use convolutional layers that apply learnable filters across input data to detect local patterns like edges, textures, and shapes. Pooling layers reduce dimensionality while preserving important features. The hierarchical structure allows CNNs to learn increasingly abstract representations, making them powerful for computer vision tasks.
Examples
More in Deep Learning
Attention Mechanism
A technique that allows models to focus on relevant parts of the input when producing output.
Dropout
A regularization technique that randomly drops neurons during training to prevent overfitting.
Fine-Tuning
Adapting a pre-trained model to a new task by training on task-specific data.
LSTM (Long Short-Term Memory)
An RNN variant with gates that control information flow, enabling learning of long-term dependencies.
Recurrent Neural Network (RNN)
A neural network architecture designed for sequential data with connections between nodes forming cycles.
Transfer Learning
Using knowledge learned from one task to improve performance on a different but related task.
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