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.
Flattening: The process of converting a 2d image into 1d vector
ReLU: An activation function that allows a model to solve nonlinear problems
Softmax: A function that provides probabilities for each possible output class
Classification: A machine learning model used for distinguishing among two or more output categories
Training Set: The data used for training the neural network.
Test set: The data used for testing the final performance of our neural network.
Regression: A model that outputs a single value. For example, an estimate of a house’s value.
Classification: A model that outputs a probability distribution across several categories. For example, in Fashion MNIST, the output was 10 probabilities, one for each of the different types of clothing. Remember, we use Softmax as the activation function in our last Dense layer to create this probability distribution.
CNNs: Convolutional neural network. That is, a network which has at least one convolutional layer. A typical CNN also includes other types of layers, such as pooling layers and dense layers.
Convolution: The process of applying a kernel (filter) to an image
Kernel / filter: A matrix which is smaller than the input, used to transform the input into chunks
Padding: Adding pixels of some value, usually 0, around the input image
Pooling The process of reducing the size of an image through downsampling.There are several types of pooling layers. For example, average pooling converts many values into a single value by taking the average. However, maxpooling is the most common.
Maxpooling: A pooling process in which many values are converted into a single value by taking the maximum value from among them.
Stride: the number of pixels to slide the kernel (filter) across the image.
Downsampling: The act of reducing the size of an image