Editing Neural Networks (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Artificial neuron''' — A mathematical unit that receives one or more inputs, applies a weighted sum, adds a bias, and passes the result through an activation function to produce an output.
* '''Layer''' — A collection of neurons that process inputs in parallel. Networks are composed of an input layer, one or more hidden layers, and an output layer.
* '''Weight''' — A learnable scalar value attached to each connection between neurons. Weights determine the strength of influence one neuron has on another.
* '''Bias''' — An additional learnable parameter added to the weighted sum before applying the activation function, allowing the neuron to shift its output independently of its inputs.
* '''Activation function''' — A mathematical function applied to a neuron's output to introduce non-linearity. Common examples: ReLU, Sigmoid, Tanh, Softmax.
* '''Forward pass''' — The process of propagating input data through the network layer by layer to produce a prediction.
* '''Backpropagation''' — An algorithm that computes the gradient of the loss function with respect to each weight, enabling gradient-based learning.
* '''Loss function''' — A measure of how far the network's predictions are from the true labels. Examples: Mean Squared Error (MSE), Cross-Entropy Loss.
* '''Gradient descent''' — An optimization algorithm that iteratively updates weights in the direction that reduces the loss.
* '''Epoch''' — One full pass through the entire training dataset.
* '''Batch size''' — The number of training examples used in a single weight update step.
* '''Learning rate''' — A hyperparameter controlling the size of weight update steps during gradient descent.
* '''Overfitting''' — When a model learns the training data too well, including its noise, and fails to generalize to new data.
* '''Dropout''' — A regularization technique where random neurons are deactivated during training to prevent overfitting.
* '''Convolutional Neural Network (CNN)''' — A network architecture specialized for grid-like data (e.g., images) using convolution operations.
* '''Recurrent Neural Network (RNN)''' — An architecture where connections form directed cycles, enabling processing of sequential data.
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