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How to use Deep Learning for Time Series Forecasting
Oct 17, 2021
07:30 PM - 09:00 PM (Asia/Kolkata)
Online, Zoom Call
Link will be shared,On your email

This event has ended. Please contact organizer for more details.

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Please Note:

  • The meetup will happen on Zoom & can be viewed from anywhere (The meetup recording will only be available on https://rewind.hellomeets.com/hellomeets
  • You will receive the link to join the meetup on your registered email id.
  • All you need is a good wifi connection and a fully charged laptop/mobile with the Zoom App installed.
  • In case you are unable to make a payment, please reach out to us at [email protected] We’ll share a PayPal link with you.
  • We also have a special discount for students. Email a copy of your student id to [email protected] to avail it.

We only have 50 tickets for this meetup. The first 15 tickets are for 399 and after the price will be 499 INR

 

Highlight:

  • How to use Deep Learning for Time Series Forecasting



Meetup Level:

  • Beginner



Walkthrough:

  • The life cycle of a deep learning model



Case study:

  • How using deep learning for load forecasting can save utilities millions



Discussion notes:

  • How keeping up with research helps your data science & ML teams
  • Time series forecasting task : traditional techniques vs deep learning
  • Inferences on applicability of deep learning to business applications


About The Speaker:

  • Mayank Sharan is studying masters in CS at University of California, San Diego
  • He is an ex-Senior Data Scientist at Bidgely Technologies
  • Mayank is also alumnus of IIT Delhi - Computer Science branch
  • He is specialized in Data Analytics & AI



Who Should Attend:

  • Anyone and everyone who is curious about deep learning.
  • SWEs
  • Data scientists
  • Product Managers
  • Company Leaders

  1. ​Online,
    Zoom Call,
    Link will be shared, Please make sure you enter the correct email id, On your email


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Organiser : Hello Meets

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How to use Deep Learning for Time Series Forecasting

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