sequence-prediction
- Named-Entity Recognition based on Neural Networks (22 Oct 2018)
This blog post reviews some of the recently proposed methods to perform named-entity recognition using neural networks. - Conditional Random Fields for Sequence Prediction (13 Nov 2017)
An introduction to Linear-Chain Conditional Random Fields: the motivation behind them, compared with Hidden-Markov and Maximum Entropy Markov models. - Maximum Entropy Markov Models and Logistic Regression (12 Nov 2017)
An introduction to the Maximum Entropy Markov Model, covering discriminative vs. generative models and its main advantages over Naive Bayes. - Hidden Markov Model and Naive Bayes relationship (11 Nov 2017)
An introduction to Hidden Markov Models, one of the first proposed algorithms for sequence prediction, and its relationships with the Naive Bayes approach.
retrieval-augmented-generation Haystack viterbi sequence-prediction evaluation-metrics scikit-learn pos-tags named-entity-recognition embeddings conditional-random-fields classification word2vec triplet-loss syntactic-dependencies sentence-transformers relationship-extraction neural-networks information-retrieval fine-tuning coursera conference SyntaxNet NLTK LSTM wikidata transformers tokenization tf-idf text-summarisation semantic-web semantic-drift resources reference-post production portuguese political-science naive-bayes multi-label-classification monitoring mlops metadata-extraction maximum-entropy-markov-models logistic-regression llms language-models information-extraction imbalanced-data hyperparameter-optimization hidden-markov-models grid-search gensim generative-ai fasttext document-classification doc2vec deployment dependency-graph dataset data-challenge convolutional-neural-networks contrastive-learning books attention SPARQL RNN PyData KONVENS GRU