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Harvard University, one of the premium educational institutions in the US, has announced several online short-term courses for programmers and computer science scholars to keep learning during the COVID-19 pandemic. 
Artificial neural networks have become state-of-the-art in the task of language modelling on a small corpora. While feed-forward networks are able to take into account only a fixed context length to predict the next word, recurrent neural networks (RNN) can take advantage of all previous words. Due the difficulties in training of RNN, the way could be in using Long Short Term Memory (LSTM) neural network architecture. In the below given pdf, we show an application of LSTM network with extensions on a language modelling task with Czech spontaneous phone calls. Experiments show considerable improvements in perplexity and WER on recognition system over n-gram baseline.
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​​CREME – python library for online ML

All the tools in the library can be updated with a single observation at a time, and can therefore be used to learn from streaming data.

The model learns from one observation at a time, and can therefore be updated on the fly. This allows to learn from massive datasets that don't fit in main memory. Online machine learning also integrates nicely in cases where new data is constantly arriving. It shines in many use cases, such as time series forecasting, spam filtering, recommender systems, CTR prediction, and IoT applications. If you're bored with retraining models and want to instead build dynamic models, then online machine learning might be what you're looking for.

Here are some benefits of using creme (and online machine learning in general):
• incremental – models can update themselves in real-time
• adaptive – models can adapt to concept drift
• production-ready – working with data streams makes it simple to replicate production scenarios during model development
• efficient – models don't have to be retrained and require little compute power, which lowers their carbon footprint


api reference: https://creme-ml.github.io/content/api.html
github: https://github.com/creme-ml/creme
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​​Practitioner’s Guide to Statistical Tests
CoreML team at VK

If you want to learn how to choose the right statistical test from the many available and run it on your own data you can find the answer at this article.

The two most essential things in A/B tests are the design of the experiments and accurate analysis of the experiments’ results. In this article, the authors stuck to the most common design and compare various statistical analysis procedures, from the very standard t-test and Mann-Whitney test to state-of-the-art approaches like the reweighted bootstrap.


article: https://medium.com/@vktech/practitioners-guide-to-statistical-tests-ed2d580ef04f
github: https://github.com/marnikitta/stattests

#statistic #ab #tests #vktech