Encoder-Decoder
An encoder-decoder is a neural network architecture where the encoder compresses input into a latent representation and the decoder generates output from it. This architecture is foundational for translation, summarization, and image captioning.
Understanding Encoder-Decoder
An encoder-decoder is a neural network architecture where an encoder component compresses input data into a latent representation and a decoder component reconstructs or generates output from that representation. This design is foundational in sequence-to-sequence tasks such as machine translation, text summarization, and speech recognition. The original architecture used recurrent neural networks, but modern implementations often use transformer-based attention mechanisms that allow the model to capture long-range dependencies more effectively. In computer vision, encoder-decoder structures power image segmentation models like U-Net and autoencoders used for dimensionality reduction and anomaly detection. Variational autoencoders extend the architecture for generative AI tasks. The encoder-decoder pattern also appears in diffusion models, where the encoder maps data to noise and the decoder learns to reverse the process, and in many foundation models that process and generate multimodal content.
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.