Sigmoid Function
The sigmoid function is an activation function that maps any input to a value between 0 and 1, making it useful for binary classification outputs. It has been largely replaced by ReLU in hidden layers but remains standard for output layers.
Understanding Sigmoid Function
The sigmoid function is a mathematical activation function that maps any real number to a value between 0 and 1, producing an S-shaped curve. Historically, it was the default activation in neural networks, valued for its smooth gradient and probabilistic interpretation. The sigmoid remains essential in binary classification output layers, where it converts raw logits into probability estimates, and in gating mechanisms within LSTM and GRU recurrent networks. However, for hidden layers in deep networks, sigmoid has largely been replaced by ReLU and its variants because sigmoid suffers from the vanishing gradient problem, where gradients become extremely small during backpropagation, slowing training. The closely related softmax function generalizes sigmoid to multi-class classification scenarios.
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.