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machine learning

How to use magnitude with keras

This time we have a look into the magnitude library, a feature-packed Python package for utilizing vector embeddings in machine learning models in a fast, efficient, and simple manner. We want to utilize the embeddings magnitude provides and use them in keras.

Enhancing LSTMs with character embeddings for Named entity recognition

This is the fifth in my series about named entity recognition with python. The last time we used a CRF-LSTM to model the sequence structure of our sentences. While this approach is straight forward and often yields strong results there are some potential shortcomings. If we haven’t seen a word a prediction time, we have to encode it as unknown and have to infer it’s meaning by it’s surrounding words. To encode the character-level information, we will use character embeddings and a LSTM to encode every word to an vector. We can use basically everything that produces a single vector for a sequence of characters that represent a word.

Guide to word vectors with gensim and keras

  Today, I tell you what word vectors are, how you create them in python and finally how you can use them with neural networks in keras. For a long time, NLP methods use a vectorspace model to represent words…. Continue Reading →

Detecting Network Attacks with Isolation Forests

In this post, I will show you how to use the isolation forest algorithm to detect attacks to computer networks in python.

Sequence tagging with a LSTM-CRF

This is the fourth post in my series about named entity recognition. The last time we used a recurrent neural network to model the sequence structure of our sentences. Now we use a hybrid approach combining a bidirectional LSTM model and a CRF model. The so called LSTM-CRF is a state-of-the-art approach to named entity recognition.

Named entity recognition with conditional random fields in python

This is the second post in my series about named entity recognition. This time, we’re going to look into a more sophisticated algorithm, a so called conditional random field.

Classifying genres of movies by looking at the poster – A neural approach

Today we will apply the concept of multi-label multi-class classification with neural networks from the last post to
classify movie posters by genre.

Guide to multi-class multi-label classification with neural networks in python

Often in machine learning tasks, you have multiple possible labels for one sample that are not mutually exclusive. This is called a multi-class, multi-label classification problem. Obvious suspects are image classification and text classification, where a document can have multiple… Continue Reading →

Getting started with Multivariate Adaptive Regression Splines

In this post we will introduce multivariate adaptive regression splines model (MARS) using python. This is a regression model that can be seen as a non-parametric extension of the standard linear model.

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