Activation Functions §

Group: 4 #group-4

Relations §

• Softmax: Softmax is a type of activation function used for multi-class classification in neural networks.
• Backpropagation: Activation functions in neural networks introduce non-linearities, which are essential for backpropagation to work effectively.
• ReLU: ReLU (Rectified Linear Unit) is a type of activation function used in neural networks.
• Squashing Function: Activation functions like sigmoid and tanh are also known as squashing functions.
• Leaky ReLU: Leaky ReLU is a variant of the ReLU activation function.
• Optimization: The choice of activation function affects the optimization process during neural network training.
• Deep Learning: Activation functions are essential components of deep learning models like deep neural networks.
• Neural Networks: Activation functions introduce non-linearity into neural networks, allowing them to model complex relationships in data.
• Neural Networks: Activation functions are used in the hidden layers of neural networks to introduce non-linearity.
• Sigmoid: Sigmoid is a type of activation function used in neural networks.
• Exploding Gradient: Some activation functions can also lead to the exploding gradient problem during training.
• Thresholding Function: Some activation functions like ReLU act as thresholding functions.
• Gradient Descent: The choice of activation function affects the performance of gradient descent optimization in neural networks.
• Non-linearity: Activation functions introduce non-linearity into neural networks, allowing them to learn complex patterns.
• Backpropagation: Activation functions play a crucial role in the backpropagation algorithm used to train neural networks.
• Machine Learning: Activation functions are important components of machine learning models like neural networks.
• Convolutional Neural Networks: Activation functions like ReLU are used in Convolutional Neural Networks to introduce non-linearity
• Artificial Neural Networks: Activation functions are used in artificial neural networks to model the firing of neurons.
• Tanh: Tanh (hyperbolic tangent) is a type of activation function used in neural networks.
• Normalization: Some activation functions like tanh can help with normalization of input data.
• ELU: ELU (Exponential Linear Unit) is a type of activation function used in neural networks.
• Vanishing Gradient: Certain activation functions like sigmoid can suffer from the vanishing gradient problem during training.