Batch Size
Deep Learning
Technique that normalizes the input of each layer to improve training stability and speed
Detailed Explanation
Batch Normalization is a technique used in deep learning to normalize the inputs of each layer during training. By adjusting and scaling the activations, it reduces internal covariate shift, leading to more stable and faster training. It helps prevent issues such as vanishing/exploding gradients, enabling faster convergence and improved model performance, especially in deep neural networks.
Use Cases
- •Accelerate training convergence and improve stability in deep neural networks for image classification tasks.
Related Terms
Other terms in the Deep Learning category