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expectation-maximization

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machine-learning

je-suis-tm / machine-learning

Star 208

Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrated Model, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, F…

naive-bayes expectation-maximization support-vector-machine ridge-regression expectation-maximization-algorithm factor-analysis dbscan em-algorithm linear-discriminant-analysis stochastic-gradient-descent latent-semantic-analysis lasso-regression multinomial-naive-bayes singular-value-decomposition batch-gradient-descent sequential-minimal-optimization newton-method low-rank-approximation independent-component-analysis k-nearest-neighbors
  • Updated Dec 16, 2022
  • Jupyter Notebook

pesoto / Text-Analysis

Star 56

Explaining textual analysis tools in Python. Including Preprocessing, Skip Gram (word2vec), and Topic Modelling.

python machine-learning text-mining neural-network word2vec word-embeddings web-scraping lstm expectation-maximization gradient-descent text-processing lda text-prediction scraping-websites latent-dirichlet-allocation skipgram gibbs-sampling long-short-term-memory-models word-embedding textual-analysis
  • Updated Sep 21, 2017
  • Jupyter Notebook

cambridge-mlg / DUN

Star 72

Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)

machine-learning deep-learning reproducible-research pytorch uncertainty neural-networks expectation-maximization uncertainty-neural-networks bayesian-inference uncertainty-quantification variational-inference bayesian-neural-networks robustness neural-architecture-search bayesian-deep-learning probabilistic-inference reproducible-paper probabilistic-neural-network learnt-depth network-depths
  • Updated Oct 3, 2023
  • Jupyter Notebook

agrawal-priyank / machine-learning-clustering-retrieval

Star 18

Built text and image clustering models using unsupervised machine learning algorithms such as nearest neighbors, k means, LDA , and used techniques such as expectation maximization, locality sensitive hashing, and gibbs sampling in Python

python machine-learning retrieval nearest-neighbor-search expectation-maximization locality-sensitive-hashing kmeans kd-trees gibbs-sampling unsupervised-machine-learning machine-learning-clustering
  • Updated Jan 20, 2018
  • Jupyter Notebook

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