Reference 7 stops to get here · leads to 1

Exploding Gradient

A problem where gradients become extremely large during backpropagation, causing unstable training and NaN values.

Your route here

7 stops · basics first
  1. Machine Learning ✓ understood

    Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.

  2. Neural Network ✓ understood

    A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.

  3. Dataset ✓ understood

    A collection of data examples used for training, validating, or testing machine learning models.

  4. Training ✓ understood

    The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.

  5. Loss Function ✓ understood

    A function that scores how wrong a model's prediction is as a single number, which training then works to make as small as possible.

  6. Gradient Descent ✓ understood

    An optimization method that repeatedly moves a model's parameters a small step in the direction that most reduces the loss.

  7. Backpropagation ✓ understood

    The algorithm for computing gradients of the loss with respect to network weights, enabling training through gradient descent.

  8. Exploding Gradient · you are here ✓ understood

Where it sits

Before this

Backpropagation
Exploding Gradient

Leads to

Gradient Clipping

Explore nearby

Training Vanishing Gradient A problem where gradients become extremely small during backpropagation, preventing deep networks from learning effectively. Training Gradient Clipping Limiting gradient magnitudes during training to prevent exploding gradients and stabilize training. Neural Networks Recurrent Neural Network A neural network architecture with loops that allow information to persist, designed for sequential data like text and time series. Neural Networks Batch Normalization A technique that normalizes layer inputs to stabilize and accelerate training by reducing internal covariate shift. Neural Networks Xavier Initialization A weight initialization strategy maintaining variance across layers, improving training of deep networks.

In the research

All papers →

A paper that builds on Exploding Gradient .