Softmax
Softmax is a function that converts a vector of raw scores into a probability distribution where all values sum to 1. It is the standard output activation for multi-class classification and attention mechanisms.
Understanding Softmax
The softmax function converts a vector of raw scores (logits) into a probability distribution where all values sum to one, with larger inputs receiving exponentially higher probabilities. It serves as the standard output activation for multi-class classification tasks in neural networks, appearing in the final layer of models for image recognition, text classification, and more. Softmax is also central to the attention mechanism in transformers, where it normalizes attention weights across sequence positions. The function's temperature parameter controls the sharpness of the distribution: lower temperatures produce more confident, peaked outputs while higher temperatures yield more uniform, exploratory distributions. During training, softmax outputs are typically paired with cross-entropy loss to drive effective gradient-based optimization through backpropagation.
Category
Deep Learning
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Activation Function
An activation function is a mathematical function applied to a neuron's output to introduce non-linearity into a neural network. Common activation functions include ReLU, sigmoid, and tanh, each with different properties for gradient flow.
Adam Optimizer
Adam (Adaptive Moment Estimation) is an optimization algorithm that combines the benefits of AdaGrad and RMSProp. It adapts learning rates for each parameter using estimates of first and second moments of gradients.
Adapter Layers
Adapter layers are small trainable modules inserted into a pre-trained model to enable parameter-efficient fine-tuning. They allow task adaptation while keeping the original model weights frozen.
Attention Mechanism
An attention mechanism allows neural networks to focus on the most relevant parts of the input when producing each element of the output. Attention is the foundational innovation behind the Transformer architecture and modern large language models.
Autoencoder
An autoencoder is a neural network trained to compress input data into a compact representation and then reconstruct it. Autoencoders are used for dimensionality reduction, denoising, and learning latent representations.
Backpropagation
Backpropagation is the algorithm used to train neural networks by computing gradients of the loss function with respect to each weight. It propagates error signals backward through the network to update weights and minimize prediction errors.
Batch Normalization
Batch normalization is a technique that normalizes layer inputs across mini-batches during training to stabilize and accelerate neural network training. It reduces internal covariate shift and allows higher learning rates.
Batch Size
Batch size is the number of training examples used in one iteration of gradient descent. Larger batches provide more stable gradient estimates but require more memory, while smaller batches add beneficial noise.