Riyanka Bhowal
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Senior Data Scientist
Walmart Global Tech |
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Riyanka is a Senior Data Scientist at Walmart Global Tech in Bangalore with nearly 6 years of experience in statistical modeling & machine learning. She has a Master's in degree Applied Statistics & Informatics from Indian Institute of Technology, Bombay (IIT Bombay). Presently, as a part of Digital Facilities team, Riyanka is developing scalable anomaly detection algorithms leveraging IoT sensor data to provide insights into store asset lifecycle decisions.
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Workshop: Natural Language Processing
June 30, 10:15-11:00 am PST
June 30, 10:15-11:00 am PST
Prerequisite: Basics of Neural Network, RNN/LSTM.
Please install Anaconda and Jupyter, and download this csv file & python notebook prior to the workshop.
Natural language processing has direct real-world applications, from speech recognition to automatic text generation, from lexical semantics understanding to question answering. In just a decade, neural machine learning models became widespread, largely abandoning the statistical methods due to its requirement of elaborate feature engineering. Popular techniques include use of word-embeddings to capture semantic properties of words. In this workshop, we take you through the ever-changing journey of neural models while addressing their boons and banes.
The workshop will address concepts of word-embedding, frequency-based and prediction-based embedding, positional embedding, multi-headed attention and application of the same in unsupervised context.
Please install Anaconda and Jupyter, and download this csv file & python notebook prior to the workshop.
Natural language processing has direct real-world applications, from speech recognition to automatic text generation, from lexical semantics understanding to question answering. In just a decade, neural machine learning models became widespread, largely abandoning the statistical methods due to its requirement of elaborate feature engineering. Popular techniques include use of word-embeddings to capture semantic properties of words. In this workshop, we take you through the ever-changing journey of neural models while addressing their boons and banes.
The workshop will address concepts of word-embedding, frequency-based and prediction-based embedding, positional embedding, multi-headed attention and application of the same in unsupervised context.
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