Overview
Network depth is a critical aspect of neural networks that influences their ability to learn and generalize from data. A deeper network can capture more complex patterns, making it suitable for challenging tasks like image and speech recognition. However, with increased depth comes the risk of overf...
Key Terms
Example: Neural networks are used in image classification tasks.
Example: The input layer receives the initial data.
Example: ReLU is a popular activation function.
Example: A model that performs well on training data but poorly on new data is overfitting.
Example: L2 regularization adds a penalty for large weights.
Example: Dropout helps improve model generalization.
Related Topics
Convolutional Neural Networks
A type of neural network particularly effective for image processing tasks.
advancedRecurrent Neural Networks
Neural networks designed for sequential data, such as time series or natural language.
advancedTransfer Learning
A technique where a pre-trained model is adapted for a new but related task.
intermediateKey Concepts
Neural NetworksLayer DepthFeature LearningOverfitting