Model Components

A class of deep learning models that have revolutionized the field of natural language processing (NLP).

The component of a transformer model responsible for generating output sequences based on encoded information.

A model architecture that uses self-attention mechanisms to improve performance on tasks involving sequential data.

Models that transform sequences from one domain to another, commonly used in translation and summarization.

Models that evaluate potential actions or responses in reinforcement learning to guide learning towards desired outcomes.

A model that retrieves relevant information from a large dataset to support decision-making or responses.

A type of neural network well-suited for processing sequences of data, like text or time series.

A model that makes predictions about unknown future events based on patterns found in historical data.

A variable in a model that is learned from the training data and determines the model's output.

A series of algorithms that mimic the operations of a human brain to recognize relationships in data.

The specific layout and structure of a machine learning model, including its layers and nodes.

A mathematical representation of a real-world process, trained to perform specific tasks using data.

The largest amount of text or data that a model can generate in response to a single prompt.

An extensive model trained on vast amounts of text data, capable of understanding and generating text.

AI that understands, interprets, and generates human language based on statistical probabilities.

The third iteration of OpenAI's generative model known for its advanced text generation capabilities.

In GANs, the component that creates data aiming to mimic real data as closely as possible.

A type of AI model specializing in generating coherent and contextually relevant text.

A type of AI model that can generate new data instances similar to the training data.

A framework for training generative models through a competitive process between networks.

A large, versatile AI model trained on a broad dataset, capable of performing multiple tasks.

A component of a model that processes and transforms input data into a usable format.

Dense vector representations of words or phrases capturing semantic meaning for AI processing.

The component of a generative adversarial network that distinguishes real data from fake.

The range of past input that a model can consider when generating a response or prediction.

Representations of words or phrases that take into account the context in which they appear.

A rectangular border used in visual processing to define the location of objects within images.

Models that use previous time points to predict future values, common in time series forecasting.

Computing systems vaguely inspired by the biological neural networks in human brains.

Interfaces that allow different software applications to communicate and work together.