by F. N . Logothetis

In data analysis, anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.

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Source : Andrew Ng-Machine Learning Course — Coursera

Three broad categories of anomaly detection techniques exist. Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances…


“Attention is all you need”

Recurrent neural networks (RNN), long short-term memory (LSTM) and gated recurrent neural networks (GRNN) in particular, have been established as state-of-the art approaches in sequence modeling and other problems, such as language modeling and machine translation.

Recurrent models are typically recurrent nodes which take as input the hidden states (h) of the previous node and a new embeddings (v). Then, they generate a sequence of new hidden sated (h’), as a function of the inputs. For each new word or character, a new hidden state is produced resulting in a long…

Fragkoulis Logothetis

Machine Learning and Software Engineer, M. Eng, M. Sc.

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