[Free ebook] - 100% discount code for the amazing book Deep learning with Pytorch for a lucky follower. Just follow our page and retweet! Only for Twitter followers! PyTorch #machinelearning #deeplearning

Our researcher Ben Wycliff Mugalu 🇺🇬🌍 exhibiting web and mobile prototypes of our work on "A Computer-Aided Diagnosis System for Cervical Cancer from Colposcopic Images". The DSA Uganda Chapter launch. #DSAU

For the past 5 weeks, 11 students have been doing their internship with us and have specialized in different areas in Machine Learning.

Back on track, with the same commitment and dedication as always! 💪🏽

the bird is freed

Our researchers, Alvin Bagetuuma Kimbowa and Paul Mugume Okwija attending the Deep Learning IndabaX Uganda Conference. #HappeningNow Deep Learning IndabaX Uganda

Bruce, Olivia and Ivan worked on Detecting fall armyworm damage in cassava leaves and A Machine Learning Evaluation Platform.

Thank you, Elon Musk ! Feels great to be back. Hope all the haters and losers have missed me!

The Uganda Communications Commission, through NCC2022 , sponsored our Lab Research Lead, Dr Andrew, to attend the recently concluded GSMA Mobile World Congress in Kigali as one of the ways to empower local solution innovators.

Anthony Fauci is 84 Klaus Schwab is 84 George Soros is 94 Nancy Pelosi is 82 McConnell is 80 Joe Biden is 80 Founding Fathers: James Monroe, 18 Aaron Burr, 20 Alexander Hamilton, 21 James Madison, 25 Thomas Jefferson, 33 John Adams, 40 George Washington, 44 Interesting

Happy #IWD2023 ! 🎉 As the Marconi Research and Innovation Lab, we are committed to leveraging technology and innovation for a more inclusive and equal world for all. Here’s to the women at Marconi Research and Innovation Lab and beyond! #IWD2023 #WomensDay2023

Dr. Joyce Nakatumba-Nabende presenting on Fairness in Healthcare and AI.

CPU vs FPGA

Hans Baier @[email protected] well, that was not exactly correct, so I fixed it to match with our actual FPGA applications! 🤣

Novel generative model! The output distribution of the model would drift over training. And if it reaches the data distribution the drift should stop. You can make a training objective based on this. Equilibrium based objective.