Dataconomy

MAY 30, 2018

Great tech events often feature a stunt or two that attendees will be talking about long after the conference hall has closed its doors; on this front, CUBE Tech Fair 2018 certainly delivered. Attendees got to witness an audience member pilot a drone with his mind (thanks to Emotiv’s President. The post Machine Learning to Mineral Tracking: The 4 Best Data Startups From CUBE Tech Fair 2018 appeared first on Dataconomy.

Machine Learning 172

Machine Learning Machine Learning Data Science 172

Depends on the Definition

JUNE 1, 2018

Often in text classification, we use so called black-box classifiers. By black-box classifiers I mean a classification system where the internal workings are completely hidden from you. A famous example are deep neural nets, in text classification often recurrent or convolutional neural nets.

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Explosion

MAY 27, 2018

This talk presents a fast, flexible and even somewhat fun approach to named entity annotation. Using our approach, a model can be trained for a new entity type in only a few hours, starting from only a feed of unannotated text and a handful of seed terms.

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Dataconomy

MAY 29, 2018

How clean is your data? If you don’t already know the answer to that question, you might be in some trouble – especially if you become the target of a cyberattack. While 2018 hasn’t seen any cyberattacks on the level of WannaCry just yet, the year is still young. I. The post To Avoid Cyberattacks, You Must Clean Your Data. Here’s Why. appeared first on Dataconomy.

Big Data 171

Big Data Big Data 171

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Explosion

MAY 27, 2018

While there is a wide literature on developing neural networks for natural language understanding, the networks all have the same general architecture. This talk explains the four components (embed, encode, attend, predict), gives a brief history of approaches to each subproblem, and explains two sophisticated networks in terms of this framework.

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Depends on the Definition

JUNE 1, 2018

In this post, I’m going to show you how you can use a neural network from keras with the LIME algorithm implemented in the eli5 TextExplainer class. For this we will write a scikit-learn compatible wrapper for a keras bidirectional LSTM model. The wrapper will also handle the tokenization and the storage of the vocabulary.

Algorithm 40

Algorithm 40