ML-key-terms.txt

Machine Learning important Terms:

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. Weights set the standards for the neuron's signal strength. This value will determine the influence input data has on the output product. Biases give extra characteristics with a value of 1 that the neural network did not previously have. The neural network needs that extra information to efficiently propagate forward.

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. (There are many algorithms for this.“Adam” Optimizer, which stands for ADAptive with Momentum is considered the best-practice optimizer.)

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.