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sidgan/README.md

Siddha Ganju

Currently, I am an Architect at Nvidia focusing on the Self-Driving initiative. I work towards stable and scalable training of neural networks on very large data centers, and utilize simulation to validate the neural networks.

In 2017 I led NASA's Long-Period Comets team within their AI accelerator, called Frontier Development Lab, where we use machine learning to develop meteor detectors. Recently this project was able to provide the first-ever instrumental evidence of an outburst of 5 meteors coming from a previously known comet, called C/1907 G1 (Grigg-Mellish). As a member of the NASA FDL AI Technical Committee, I'm working towards incorporating AI in many space science projects!

I have also authored a book on Practical Deep Learning for Cloud, Mobile & Edge - O'Reilly Publishers




** Featured as a learning resource on the official Keras website**

[Online on Safari] | [Buy on Amazon] | [Online on Google Books] | [Book Website] | [Presentation on Slideshare]

Pinned

  1. whats_in_a_question whats_in_a_question Public

    CVPR'17 Spotlight: What’s in a Question: Using Visual Questions as a Form of Supervision

    Lua 44 8

  2. PracticalDL/Practical-Deep-Learning-Book PracticalDL/Practical-Deep-Learning-Book Public

    Official code repo for the O'Reilly Book - Practical Deep Learning for Cloud, Mobile & Edge

    Jupyter Notebook 724 304

  3. ETCI-2021-Competition-on-Flood-Detection ETCI-2021-Competition-on-Flood-Detection Public

    Experiments on Flood Segmentation on Sentinel-1 SAR Imagery with Cyclical Pseudo Labeling and Noisy Student Training

    Jupyter Notebook 153 27

  4. spaceml-org/GIBS-Downloader spaceml-org/GIBS-Downloader Public

    GIBS Downloader is a command-line tool which facilitates the downloading of NASA satellite imagery and offers different functionalities in order to prepare the images for training in a machine lear…

    Jupyter Notebook 44 8

  5. spaceml-org/Self-Supervised-Learner spaceml-org/Self-Supervised-Learner Public

    Curator can be used to train a classifier with fewer labeled examples needed using self-supervised learning.

    Jupyter Notebook 109 26