Sequence-to-Sequence
Sequence-to-sequence (Seq2Seq) is a model architecture that transforms one sequence into another, used in translation, summarization, and dialogue. It consists of an encoder that reads the input and a decoder that generates the output.
Understanding Sequence-to-Sequence
Sequence-to-sequence (seq2seq) models transform an input sequence into an output sequence, making them ideal for tasks where input and output lengths differ. Originally built with encoder-decoder recurrent neural networks, seq2seq architectures gained an attention mechanism that allowed the decoder to focus on relevant parts of the input at each generation step. This framework powers machine translation systems like Google Translate, text summarization engines, conversational AI, and speech recognition pipelines. The transformer architecture later replaced RNN-based seq2seq models, dramatically improving parallelization and long-range dependency handling. Modern large language models can be viewed as advanced sequence-to-sequence systems, mapping input prompts to generated responses through billions of learned parameters and sophisticated decoding strategies.
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.