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Tag: LVQ

Evolution of NLP Algorithms through Latent Variables: Future of AI (Part 3 of 3)

AJM Note: ALTHOUGH NEARLY COMPLETE, references and discussion still being added to this blogpost. This note will be removed once the blogpost is completed – anticipated over the Memorial Day weekend, 2023. Note updated 12:30 AM, Hawai’i Time, Tuesday, May 29, 2023. This blogpost accompanies a YouTube vid on the same topic of the Evolution… Continue reading Evolution of NLP Algorithms through Latent Variables: Future of AI (Part 3 of 3)

Published May 22, 2023
Categorized as A Resource, A Resource - Annotated Collection, A Resource - Papers, A Resource - Prior Blogposts, A Resource - YouTube, Artificial Intelligence, Future of AI, Large Language Models, Latent Variables, Natural Language Processing, Neural Networks, Neural Networks Learning Methods, NN - Backpropagating Multilayer Perceptron (MLP), NN - Boltzmann Machine, NN-Learning - Contrastive Divergence, NN-Learning - Stochastic Gradient Descent Backpropagation, Statistical Mechanics Tagged Andrew Ng, BERT, Bidirectional Encoder Representations from Transformers, ChatGPT, D2V, David Blei, Doc2Vec, encoder, Geoffrey Hinton, GPT, GPT 3.0, GPT 4.0, Kunihiko Fukushima, Latent Dirichlet Allocation, latent variables, LDA, Least Vector Quantization, LLM, LVQ, Michael Jordan, neural network, NLP, Paul Werbos, stochastic gradient descent, Teuvo Kohonen, transformer, Transformers, W2V, Word2Vec, Yann LeCun, Yoshio Bengio

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