Word embeddings are representations of words in a vector space that encode the meaning of words such that similar words are close together. They can be generated using various techniques such as neural networks, dimensionality reduction and probabilistic models. Word embeddings have been shown to improve performance in NLP tasks such as syntactic parsing and sentiment analysis.
Carnegie Mellon University
Spring 2021
Focused on computational systems for human languages, this course introduces various NLP applications, such as translation and summarization. It encompasses a broad scope, from machine learning to linguistics, with a software engineering perspective.
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+ 28 more conceptsStanford University
Fall 2022
An in-depth course focused on building neural networks and leading successful machine learning projects. It covers Convolutional Networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. Students are expected to have basic computer science skills, probability theory knowledge, and linear algebra familiarity.
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+ 35 more conceptsPrinceton University
Spring 2023
This course introduces the basics of NLP, including recent deep learning approaches. It covers a wide range of topics, such as language modeling, text classification, machine translation, and question answering.
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+ 13 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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+ 40 more concepts