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Data Augmentation Techniques for Text Classification in NLP (Research Paper Walkthrough)
This paper discusses Easy-to-use Data Augmentation techniques for the use-case of Text Classificatio

Aug 27

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Node2Vec: Scalable Feature Learning for Networks | ML with Graphs
This paper is one of the foundational papers in the field of graph neural networks called Node2Vec,

Aug 25

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LSBert: A Simple Framework for Lexical Simplification
This paper proposes a BERT based lexical simplification framework that aims to replace complex words

Aug 24

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graph2vec: Learning Distributed Representations of Graphs | ML with Graphs
Hello there,graph2vec proposes a technique to embed entire graph in high dimension vector space. It

Aug 20

How to decide when to re-train the Machine Learning models?

#machinelearning #aiModel drifting is a common phenomenon that you will often find to be happening with your models if you don't train them periodically. 🔥1. How to know when to re-train your ML model? 🤔2. How to evaluate your machine learning models in production? 🤔3. What should be the re-training strategy? 🤔This blog walkthrough discusses exactly these. Check out at https://www.youtube.com/watch?v=-53lR0LajHI⏩ Blog 1: The Ultimate Guide to Model... more

Aug 17

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KeyBERT: Keyword Extraction using BERT (Decoding NLP Libraries)
Hello there, #Shorts #BERT #nlp KeyBERT is an easy-to-use keyword/keyphrase extraction method that l

Aug 16

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KeyBERT: Keyword Extraction using BERT (Decoding NLP Libraries)
How to decide when to re-train the Machine Learning models?
graph2vec: Learning Distributed Representations of Graphs | ML with Graphs
LSBert: A Simple Framework for Lexical Simplification
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Node2Vec: Scalable Feature Learning for Networks | ML with Graphs
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