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.
Understanding Backpropagation
Backpropagation is the foundational algorithm that makes training deep neural networks possible, efficiently computing how each weight should be adjusted to reduce prediction errors. The process propagates the gradient of the loss function backward through the network using the chain rule of calculus, from the output layer through each hidden layer. During training, a forward pass produces predictions, the loss function quantifies error, and backpropagation calculates gradients for every parameter. These gradients are then used by an optimizer like the Adam optimizer or stochastic gradient descent to update weights toward minimizing loss. Challenges include vanishing and exploding gradients, which can stall training in very deep networks and led to innovations like batch normalization, residual connections, and careful activation function choices. Combined with GPU-accelerated computing via CUDA, backpropagation enabled the deep learning revolution that transformed computer vision and natural language processing.
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.
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.
Boltzmann Machine
A Boltzmann machine is a stochastic recurrent neural network that can learn a probability distribution over its inputs. Restricted Boltzmann Machines (RBMs) were influential in the deep learning revolution as building blocks for deep belief networks.