Dropout
Dropout is a regularization technique that randomly deactivates a fraction of neurons during training to prevent overfitting. It forces the network to learn redundant representations and improves generalization.
Understanding Dropout
Dropout is a regularization technique used during neural network training that randomly deactivates a fraction of neurons in each forward pass, forcing the network to learn more robust and distributed representations. By preventing any single neuron from becoming overly specialized, dropout reduces overfitting and improves generalization to unseen data. Introduced by Geoffrey Hinton and colleagues, dropout has become a standard component in deep learning architectures, particularly in fully connected layers and some convolutional neural network designs. During inference, all neurons are active but their outputs are scaled to compensate for the dropout rate used in training. Dropout can be interpreted as implicitly training an ensemble of many sub-networks simultaneously, which relates it conceptually to ensemble learning. It is commonly combined with other regularization methods and tuned as a hyperparameter during cross-validation.
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.