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Machine Learning Terms
Part of Machine Learning
- Feature: The input(s) to our model
- Examples: An input/output pair used for training
- Labels: The output of the model
- Layer: A collection of nodes connected together within a neural network.
- Model: The representation of your neural network
- Dense and Fully Connected (FC): Each node in one layer is connected to each node in the previous layer.
- Weights and biases: The internal variables of model
- Loss: The discrepancy between the desired output and the actual output
- MSE: Mean squared error, a type of loss function that counts a small number of large discrepancies as worse than a large number of small ones.
- Gradient Descent: An algorithm that changes the internal variables a bit at a time to gradually reduce the loss function.
- Optimizer: A specific implementation of the gradient descent algorithm.
- Learning rate: The “step size” for loss improvement during gradient descent.
- Batch: The set of examples used during training of the neural network
- Epoch: A full pass over the entire training dataset
- Forward pass: The computation of output values from input
- Backward pass (backpropagation): The calculation of internal variable adjustments according to the optimizer algorithm, starting from the output layer and working back through each layer to the input.