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Word-Embeddings-and-Document-Vectors
An evaluation of word-embeddings for classificationBow-to-Bert
Evolution of word vectors from long, sparse, and 1-hot to short, dense, and context sensitiveConcept-Drift-and-Model-Decay
Evaluating model decay as the underlying concepts for classification evolveBoW-vs-BERT-Classification
Comparing traditional classifiers with bag-of-words approach to BERT for text classificationELK-Stack-with-Vagrant-and-Ansible
Building an ELK stack with Vagrant and AnsibleNonlinear-Classification-with-Logistic-Regression
Kafka-Streams-Catching-Data-in-the-Act
An implementation of Kafka Streamscontext-aware-word-vectors
Context aware word vectorsEvaluating-Document-Transformations-for-Clustering-Text
Serving-Flask-on-Docker
Dockerizing a Flask web application behind gunicorn and NginxMulticlass-classification-with-word-bags-and-word-strings
Clustering-text-with-tranformed-document-vectors
Clustering Text with Transformed Document Vectorsword-bags-vs-word-sequences-for-text-classification
Evaluating sequence respecting approaches like LSTM against traditional bag-of-words approaches for textVirtual-Clusters-with-Vagrant-Virtualbox
Virtual Clusters with Vagrant & VirtualboxReconciling-Data-Shapes-and-Parameter-Counts-in-Keras
Deriving formulae for parameter counts when using convolutional layersChoosing-the-Optimal-Number-of-Clusters-for-K-Means
Detecting the optimal number of clusters to use with K-meansFracking-Features-in-Machine-Learning
Detecting data evolution and the need for new classesELK-Clusters-on-AWS-with-Ansible
Building ELK Clusters on AWS with Ansiblecomputing-on-coupled-data-streams-with-Beam
Coupled data streams need to be analyzed together ensuring simultaneity of events across the participant streamsAttention-as-Dynamic-Tf-Idf-for-Deep-Learning
Implementing attention to recover task specific weighting of wordsLove Open Source and this site? Check out how you can help us