Activation Functions

Activation Steering involves adjusting activation functions to optimize AI model performance.

The Dead Neuron Problem occurs when neurons in a neural network become inactive, affecting performance and learning.

The Dead ReLU Problem occurs when ReLU activation units output zero, hindering neural network learning.

ELU Activation is a neural network activation function that enhances model performance by addressing the dying ReLU problem.

A Gated Linear Unit (GLU) is a type of neural network activation function that combines linear transformations with gating mechanisms.

GEGLU is a neural network activation function combining gated mechanisms with exponential linear units.

GELU (Gaussian Error Linear Unit) is an activation function used in neural networks to improve performance.

The hyperbolic tangent function, or tanh, is a mathematical function that maps real numbers to values between -1 and 1.

Leaky ReLU is an activation function that allows a small, non-zero gradient when the input is negative.

A logistic curve models growth that saturates at a maximum limit, widely used in AI for activation functions and prediction models.

The logit function is a mathematical function used to model probabilities in binary classification problems.

A Maxout Unit is a type of activation function used in neural networks that helps improve model performance.

Mish Activation is an advanced activation function used in neural networks, promoting better training performance.

Neuron activation refers to the process by which neurons in a neural network respond to input signals, influencing the network's output.

Neuron output refers to the signal generated by a neuron after processing inputs, crucial in neural network operations.

Neuron saturation occurs when a neuron in a neural network reaches its maximum output capacity.

Non-linear activation functions introduce non-linearity in neural networks, allowing them to model complex patterns.

Output activation refers to the final layer's activation function in a neural network, determining the output format.

An output neuron is the final node in a neural network that produces the model's predictions.

SELU (Scaled Exponential Linear Unit) is an activation function designed for neural networks, promoting self-normalization.

A sigmoid is a mathematical function that produces an S-shaped curve, commonly used in AI for activation in neural networks.

SwiGLU is a neural network activation function combining the Swish and GLU functions for improved performance.

Tanh is a mathematical function that outputs values between -1 and 1, useful in machine learning and neural networks.