A language model is a probability distribution over sequences of words, assigning probabilities to different word sequences. Language models are trained on text corpora and can be used for various applications in computational linguistics. Large language models consisting of deep neural networks have become popular in recent years, and n-gram language models make use of the Markov assumption to model word sequences.
Stanford University
Winter 2023
CS 224N provides an in-depth introduction to neural networks for NLP, focusing on end-to-end neural models. The course covers topics such as word vectors, recurrent neural networks, and transformer models, among others.
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+ 21 more conceptsBrown University
Spring 2022
Brown University's Deep Learning course acquaints students with the transformative capabilities of deep neural networks in computer vision, NLP, and reinforcement learning. Using the TensorFlow framework, topics like CNNs, RNNs, deepfakes, and reinforcement learning are addressed, with an emphasis on ethical applications and potential societal impacts.
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