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The favored ML Olympiad is again for its third spherical with over 20 community-hosted machine studying competitions on Kaggle.
The ML Olympiad – organised by teams together with ML GDE, TFUG, and different ML communities – goals to offer builders with hands-on alternatives to study and apply machine studying expertise by tackling real-world challenges.
Over the earlier two rounds, a powerful 605 groups participated throughout 32 competitions, producing 105 discussions and 170 notebooks.
This 12 months’s lineup consists of challenges spanning areas like healthcare, sustainability, pure language processing (NLP), pc imaginative and prescient, and extra. Competitions are hosted by skilled teams and builders from all over the world.Listed here are this 12 months’s challenges:
Smoking Detection in Sufferers
Hosted by Rishiraj Acharya (AI/ML GDE) in collaboration with TFUG Kolkata, this competitors duties members with predicting smoking standing utilizing bio-signal ML fashions.
Organised by Anas Lahdhiri below MLAct, this problem requires the event of a classification mannequin to distinguish between jellyfish and plastic air pollution in ocean imagery.
Detect Hallucinations in LLMs
Luca Massaron (AI/ML GDE) presents a novel problem of figuring out hallucinations in solutions offered by a Mistral 7B instruct mannequin.
Anushka Raj, alongside TFUG Hajipur, seeks ML options to mitigate meals wastage, a vital concern in at this time’s world.
Hosted by Ankit Kumar Verma and TFUG Prayagraj, this competitors includes predicting the share of physique fats in males utilizing a number of regression strategies.
Ayush Morbar from Offbeats Byte Labs invitations members to construct a regression mannequin to foretell the age of crabs.
TFUG Nashik challenges members to forecast the climate situation in Nashik, India, leveraging machine studying strategies.
Predicting Earthquake Injury
Usha Rengaraju presents a activity of predicting the extent of injury to buildings attributable to earthquakes, based mostly on numerous components.
Forecasting Bangladesh’s Climate
TFUG Bangladesh (Dhaka) goals to foretell rainfall, common temperature, and wet days for a specific day in Bangladesh.
CO2 Emissions Prediction Problem
Md Shahriar Azad Evan and Shuvro Pal from TFUG North Bengal search to foretell CO2 emissions per capita for 2030 utilizing world improvement indicators.
Kuan Hoong (AI/ML GDE) challenges members to foretell mortgage approval standing, addressing an important facet of monetary inclusion.
Ashwin Raj and BeyondML activity members with predicting the habitability rating of properties, selling sustainable city improvement.
Poisonous Language (PTBR) Detection
Hosted in Brazilian Portuguese, this problem by Mikaeri Ohana, Pedro Gengo, and Vinicius F. Caridá (AI/ML GDE) includes classifying poisonous tweets.
Bettering Catastrophe Response
Yara Armel Need of TFUG Abidjan invitations members to foretell humanitarian support contributions in response to disasters worldwide.
Kartikey Rawat from TFUG Durg requires the event of predictive fashions to estimate visitors density in city areas.
Know Your Buyer Opinion
TFUG Surabaya presents a problem of classifying buyer opinions into Likert scale classes.
Forecasting India’s Climate
Mohammed Moinuddin and TFUG Hyderabad activity members with predicting temperatures for particular months in India.
Hosted by TFUG Bhopal, this competitors includes creating classification fashions to foretell tumour malignancy.
AI-Powered Job Description Generator
Akaash Tripathi from TFUG Ghaziabad challenges members to construct a system that routinely generates job descriptions utilizing Generative AI and chatbot interface.
Machine Translation French-Wolof
GalsenAI presents a problem of precisely translating French sentences into Wolof, providing a platform to boost language translation capabilities.
Water Mapping utilizing Satellite tv for pc Imagery
Taha Bouhsine of ML Nomads duties members with water mapping utilizing satellite tv for pc imagery for dam drought detection.
Google is supporting every group host this spherical by way of its Google for Builders program.
Contributors are inspired to seek for “ML Olympiad” on Kaggle, comply with #MLOlympiad on social media, and become involved within the competitions that almost all curiosity them.
With such a various array of real-world machine studying challenges, the ML Olympiad represents a wonderful alternative for builders to place their expertise to the check and acquire helpful expertise.
(Picture Credit score: Google)
See additionally: Microsoft: China plans to disrupt elections with AI-generated disinformation
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