The DataHour: Writing Reproducible Pipelines for Training Neural Networks

Dhruv Aggarwal 23 Jun, 2022 • 2 min read

Hey Readers,

We’re getting Andrey Lukyanenko, Kaggle Grandmaster, on board to lead an interactive DataHour session with us. He is working as a Senior Data Scientist with the IT consulting and solutions firm Careem. He has more than ten years of extensive experience in the field of analytics and data science. He will be explaining the Neural Networks, Writing Reusable and Reproducible Pipelines for Training Neural Networks. So, book your seat now! We know you don’t want to experience FOMO.

BOOK YOUR SEAT NOW! 🛋️

About the DataHour

Data science is concerned with reproducibility and companies all over the world are investing heavily to explore and build data science applications.

In this DataHour, Join Andrey demonstrating a working deep learning training pipeline using PyTorch-lightning as a wrapper for PyTorch code and Hydra for configuration file management.

About the Speaker

Andrey Lukyanenko works as a Senior Data Scientist for the IT solutions and consulting firm Careem. He has more than ten years of extensive experience in the field of analytics and data science. He wants to create Deep Learning apps that could benefit users and customers by making their lives better while also adding value to the business.

He is the Grandmaster of Kaggle Notebooks (ranked first in the kernel ranking), Kaggle Discussions, and Kaggle Competitions.

Prerequisites

A passion for understanding data science and a working mastery of both machine learning and Python.

Who is this DataHour for?

Those students interested in a career in data science
Professionals in data science who want to advance more quickly

Any Questions?

Email us at [email protected], or you can chat with the speaker directly during the session.

Conclusion

Register for the DataHour Here to take advantage of this wonderful opportunity.

Visit our YouTube Channel to view the recordings if you missed any of the previous episodes of “The DataHour.” On our blog, you may read a summary of the DataHour sessions that have already been held Here.