HydPy & PyLadiesHyd collab meetup - July 2020 | Theme - ML
Details
We are glad to announce our next meetup in collaboration with PyLadiedHyd and IBM. We'll be organizing this meetup themed around Machine Learning and we have two awesome SMEs giving interesting talks.
- NLP using Tensorflow 2.0 (Hands-on Workshop)
Speaker Name - Usha Rengaraju (https://in.linkedin.com/in/usha-rengaraju-b570b7a2)
Time - 6:00 pm - 7:30 pm
Talk Abstract - "Natural Language Processing(NLP) is the field of Artificial Intelligence that deals with text analysis and understanding. Sentiment classification, Spam detection and Topic detection are some of the use cases which are widely used. This workshop will get you started with real-world NLP projects using deep learning with TensorFlow 2.0.
The agenda for the workshop will be as below
Quick Refresher to Deep Learning
Quick Refresher to NLP Concepts
Introduction to TF 2.0 APIs
[HandsOn] Text Classification
[HandsOn] Poetry Creation
[HandsOn]Bangla article classifier using TF-Hub"
- Removing Unfair Bias in Machine Learning
Speaker Name - Upkar Lidder (https://www.linkedin.com/in/lidderupk)
Time - 7:30 pm - 8:15 pm IST
Talk Abstract - "Extensive evidence has shown that AI can embed human and societal bias and deploy them at scale. And many algorithms are now being reexamined due to illegal bias. So how do you remove bias & discrimination in the machine learning pipeline? In this webinar, you’ll learn the debiasing techniques that can be implemented by using the open-source toolkit AI Fairness 360.
AI Fairness 360 (AIF360) is an extensible, open-source toolkit for measuring, understanding, and removing AI bias. AIF360 is the first solution that brings together the most widely used bias metrics, bias mitigation algorithms, and metric explainers from the top AI fairness researchers across industry & academia. In this webinar you’ll learn:
How to measure bias in your data sets & models
How to apply the fairness algorithms to reduce bias
How to apply a practical use case of bias measurement & mitigation in a data-driven medical care management scenario"
We'd like to thank IBM & PyLadiesHyd for collaborating with us on this.
