Glossary

This glossary contains key machine learning and deep learning terms, with each entry on its own page for detailed explanations.

• Convolutional Neural Network - deep-learning model which specializes in image recognition
• Dropout - TBD
• Gradient Descent - TBD
• Logit - direct result of the linear operations (weighted sums of inputs plus bias) in the final dense layer of a neural network
• Loss functions - TBD
• Mini-Batch Gradient Descent - Processing data in batches to optimize neural network training
• Momentum -

  Momentum paper

• Non-Linearity - TBD
• Normalization - The process of centering data and equalizing variance across features
• Onehot Encoding - TBD
• Overfitting - TBD
• Regularization - TBD
• ReLU - Simple and widely-used activation function
• Sigmoid - TBD
• tanh - TBD
• Tensor - Mathematical concept representing multi-dimensional data structures