Machine Learning
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Great project and Initiative by The MAD & Women4.0 leaders(Chennai)!
Check the book πŸ‘†
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.
Super duper NLP REPO ✌️
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Kickstart your chatbot journey with our 6 Days Workshop starting from 12th June.

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The revolution of bots taking shape!

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Enroll now in just Rs 16 per day.

πŸ”— Link for enroll is here...

https://allevents.in/online/80002220391294


Other details on the above link πŸ‘†
Join telegram community The Madian to avail your discount.
​​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):
β€’ incremental – models can update themselves in real-time
β€’ adaptive – models can adapt to concept drift
β€’ production-ready – working with data streams makes it simple to replicate production scenarios during model development
β€’ 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