Hello ladies! We are hosting a women-only mixer for female founders, operators & VCs in AI, Machine Learning and Data Science. In the last 4 months, we had a Demo Day Meetup in February, a Deep Coffee Hack, a Hackathon and an April meetup which saw some amazing sessions & demos. This May we have two back-to-back meetups planned on the same day (a women-only mixer followed by an event open to all - both at the same venue!):
03:00 pm - 04:30 pm - Women in AI mixer (you should register for this using the “REGISTER” option on the right to get access to both events)
04:30 pm - 06:00 pm - Visual Media Gen AI meetup (you will automatically be invited to this if you register for the women-only mixer, but in case you want to learn more about it, here is the link : https://hasgeek.com/generativeAI/may-meetup/)
We will have networking sessions, group discussions & curated talks from senior women leaders in AI. Speaker names will be revealed soon, but here’s what you can expect :
🌐 Empowering Topics: Unearth the vast potential of AI in diverse commercial applications & delve into trailblazing agentic systems like AutoGPT and multi-agent async systems, including ControlNet and Offset Noise, that are revolutionizing the AI domain.
🔧 Essential Tools & Concepts: Familiarize yourself with indispensable resources like Vector DBs and immerse yourself in the world of state-of-the-art LLM-distillations, featuring Vicuna and Alpaca, that are shaping the future of AI.
🌟 Networking Opportunities for Women in AI: Engage in meaningful conversations, share experiences, learn from others, and expand your professional network with like-minded women in the industry!
Open to all women - whether you are an AI enthusiast, beginner or advanced!
Time-Series Models for Demand Forecasting
Lavanya Tekumalla is currently an ML Consultant at Sortly. With an impressive 15+ years of ML exp at giants such as Amazon, InMobi, Myntra, and Kenome, and a PhD in ML from IISc, Lavanya brings a depth of industry + academia knowledge to the table.
In this talk, she shares her experience of taming different time-series models for specific forecasting tasks.