Embedding
An embedding is a dense vector representation that captures the semantic meaning of data like words, sentences, or images in a continuous mathematical space. Similar items are mapped to nearby points, enabling semantic search and comparison.
Understanding Embedding
An embedding is a dense, low-dimensional vector representation of data that captures semantic meaning and relationships in a continuous space. In natural language processing, word embeddings like Word2Vec and GloVe map words so that semantically similar terms are positioned close together, while transformer-based models like GPT and BERT produce contextual embeddings that vary based on surrounding text. Embeddings are also used for images, audio, graphs, and user profiles, enabling similarity search, recommendation systems, and clustering. They serve as the input layer for many deep learning architectures and are fundamental to how large language models understand and generate text. The quality of an embedding directly impacts downstream tasks like classification, retrieval-augmented generation, and semantic search. Embedding spaces also support dimensionality reduction and visualization of complex, high-dimensional data.
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.