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Divum NPD Meet: Transfer Learning in TensorFlow

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Muthanna P M.
Divum NPD Meet: Transfer Learning in TensorFlow

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As part of our next NPD Meet, we are hosting a Hackathon event focussed on building Machine Learning solutions for real-world problems and a tech seminar on "Transfer Learning in TensorFlow" by Sayak Paul.

Transfer learning is a Machine Learning (ML) method used where a ML model built for a particular use case is reused as the starting point for a model on another use case. Join us to explore this ML method that is used for Deep Learning extensively.

Sayak is a Machine Learning Engineer at Carted, working on representation learning and MLOps. He is passionate about open-source and has contributed to Keras, KerasCV, Hugging Face Transformers, etc. Off the work, he binge-watches things on Netflix.

Also, get a chance to look at innovative ideas being conceptualized and built in a 24 hours Hackathon by teams of engineers, product designers and ML enthusiasts to co-create solutions for real-world problems using ML.

Join this NPD Meet on 10 Sep (Sat), 11:45 am to 12 pm.
To attend the seminar, RSVP on Meetup or register on this link - bit.ly/npdmeet

Online Link for members outside Bangalore will be shared on your email after you register on the above link.

For any clarification, please reach out to muthanna@divum.in / +91-8197265056.

About NPD Meet:
Divum's New Product Developers Meet is all about sharing & learning best practices for building new products grounds up for startups & high impact digital transformation for enterprises in the current world scenarios. We aim to bring various industry experts from Google, Amazon, Truecaller, disruptive startups & enterprises and exchange notes on technology, user-experience & go-to-market.

COVID-19 safety measures

Masks required
COVID-19 vaccination required
Event will be indoors
The event host is instituting the above safety measures for this event. Meetup is not responsible for ensuring, and will not independently verify, that these precautions are followed.
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