Long Short-Term Memory
A type of RNN architecture with gates that can learn long-term dependencies, solving the vanishing gradient problem.
Your route here
10 stops · basics first
- 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.
- 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.
- Recurrent Neural Network ✓ understood
A neural network architecture with loops that allow information to persist, designed for sequential data like text and time series.
- Activation Function ✓ understood
A non-linear function applied to neuron outputs that introduces non-linearity, enabling networks to learn complex patterns.
- Dataset ✓ understood
A collection of data examples used for training, validating, or testing machine learning models.
- 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.
- 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.
- Gradient Descent ✓ understood
An optimization method that repeatedly moves a model's parameters a small step in the direction that most reduces the loss.
- Backpropagation ✓ understood
The algorithm for computing gradients of the loss with respect to network weights, enabling training through gradient descent.
- Vanishing Gradient ✓ understood
A problem where gradients become extremely small during backpropagation, preventing deep networks from learning effectively.
- Long Short-Term Memory · you are here ✓ understood
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All papers →A paper that builds on Long Short-Term Memory .
