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What is stemming in NLP?
Stemming is the process of reducing words to their root or base form by removing affixes, such as prefixes or suffixes, to normalize variations of words.
Stemming is the process of reducing words to their root or base form by removing affixes, such as prefixes or suffixes, to normalize variations of words.
See lessWhat is tokenization in NLP?
Tokenization is the process of breaking down a sequence of text into smaller units, such as words, subwords, or characters, to facilitate further processing or analysis.
Tokenization is the process of breaking down a sequence of text into smaller units, such as words, subwords, or characters, to facilitate further processing or analysis.
See lessWhat is autoencoder in deep learning?
Autoencoder is a type of neural network architecture used for unsupervised learning of efficient data representations by training the network to reconstruct the input data from a compressed latent representation.
Autoencoder is a type of neural network architecture used for unsupervised learning of efficient data representations by training the network to reconstruct the input data from a compressed latent representation.
See lessWhat is generative adversarial networks (GANs)?
Generative adversarial networks are a type of deep learning model consisting of two neural networks, a generator and a discriminator, that are trained simultaneously in a competitive fashion to generate realistic synthetic data.
Generative adversarial networks are a type of deep learning model consisting of two neural networks, a generator and a discriminator, that are trained simultaneously in a competitive fashion to generate realistic synthetic data.
See lessWhat is transfer learning in deep learning?
Transfer learning is a machine learning technique where knowledge gained from training on one task or dataset is transferred and applied to a different but related task or dataset, often resulting in improved performance with less training data.
Transfer learning is a machine learning technique where knowledge gained from training on one task or dataset is transferred and applied to a different but related task or dataset, often resulting in improved performance with less training data.
See lessWhat are long short-term memory (LSTM) networks?
Long short-term memory networks are a type of recurrent neural network architecture specifically designed to address the vanishing gradient problem and capture long-term dependencies in sequential data.
Long short-term memory networks are a type of recurrent neural network architecture specifically designed to address the vanishing gradient problem and capture long-term dependencies in sequential data.
See lessWhat are recurrent neural networks (RNNs)?
Recurrent neural networks are a type of deep neural network designed for processing sequential data, where connections between nodes form directed cycles, allowing the network to maintain internal state and handle variable-length sequences.
Recurrent neural networks are a type of deep neural network designed for processing sequential data, where connections between nodes form directed cycles, allowing the network to maintain internal state and handle variable-length sequences.
See lessWhat are convolutional neural networks (CNNs)?
Convolutional neural networks are a type of deep neural network specifically designed for processing structured grid-like data, such as images, by applying convolutional filters to capture spatial patterns.
Convolutional neural networks are a type of deep neural network specifically designed for processing structured grid-like data, such as images, by applying convolutional filters to capture spatial patterns.
See lessWhat is the Bellman equation in reinforcement learning?
The Bellman equation is a recursive equation that expresses the value of a state or state-action pair in terms of the immediate reward and the value of the next state (or next state-action pair).
The Bellman equation is a recursive equation that expresses the value of a state or state-action pair in terms of the immediate reward and the value of the next state (or next state-action pair).
See lessWhat is the Markov decision process (MDP) in reinforcement learning?
The Markov decision process is a mathematical framework used to model decision-making in environments where outcomes are stochastic and depend only on the current state and action.
The Markov decision process is a mathematical framework used to model decision-making in environments where outcomes are stochastic and depend only on the current state and action.
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