Overview
The vanishing and exploding gradients problem is a significant challenge in training deep neural networks. It occurs when gradients become too small or too large, leading to ineffective learning. Understanding this problem is crucial for anyone working with neural networks, as it can severely impact...
Key Terms
Example: In optimization, gradients indicate how to adjust weights.
Example: Backpropagation updates weights based on the error gradient.
Example: In deep networks, early layers may learn very slowly.
Example: Exploding gradients can lead to NaN values in weights.
Example: ReLU is a popular activation function that helps mitigate vanishing gradients.
Example: ReLU helps prevent vanishing gradients in deep networks.
Related Topics
Neural Network Architectures
Explore different architectures like CNNs and RNNs that can be affected by gradient issues.
intermediateOptimization Algorithms
Learn about various optimization algorithms that can help mitigate gradient problems.
intermediateDeep Learning Techniques
Study advanced techniques in deep learning that address gradient-related challenges.
advancedKey Concepts
Gradient DescentBackpropagationActivation FunctionsNeural Networks