Residual Network
A Residual Network (ResNet) is a deep neural network architecture that uses skip connections to enable training of very deep networks. ResNets solved the vanishing gradient problem and enabled networks with hundreds of layers.
Understanding Residual Network
A Residual Network (ResNet) is a deep neural network architecture that introduces skip connections, also called shortcut connections, which allow input to bypass one or more layers by adding the original input directly to the layer's output. This innovation, introduced by Microsoft Research in 2015, solved the degradation problem where deeper networks paradoxically performed worse than shallower ones despite having greater capacity. By learning residual functions (the difference between desired output and input) rather than full transformations, ResNets enable training of networks with hundreds or even thousands of layers. ResNet architectures achieved breakthrough performance on ImageNet and became the backbone of countless computer vision systems for image classification, object detection, and segmentation. The skip connection principle has since influenced virtually all modern deep learning architectures, including transformers used in natural language processing, where residual connections around each attention and feedforward sublayer are essential for stable training.
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.