Тренировки ML

Build and train a reinforcement learning (RL) model on AWS to autonomously drive JPL’s Open-Source Rover between given locations in a simulated Mars environment with the least amount of energy consumption and risk of damage.

Масштабный проект из трех задач и итоговой конференции. Задачи включают классический табличный ML на покупках для моделирования uplift-а, построение рекомендательной системы в контейнерном формате, а также алгоритмическая задача по оптимизации расстановки товаров.

Recognize layouts such as illustrations and text lines from the digitized material data of the National Diet Library!

The goal of this challenge is to accelerate the development of more accurate, relevant, and usable open-source AI models to support mapping for disaster risk management in African cities.

The objective of this competition is to create a machine learning model to classify fields by crop type from images collected during the growing season by the Sentinel-2 satellite. The fields pictured in this training set are across western Kenya, and the images were collected by the PlantVillage team.

The objective of this challenge is to build a machine learning algorithm to correctly classify if a plant is healthy, has stem rust, or has leaf rust.

For this competition, you’re given the image of a handwritten Bengali grapheme and are challenged to separately classify three constituent elements in the image: grapheme root, vowel diacritics, and consonant diacritics.

In this competition, you’re challenged to build a machine learning model that predicts which Tweets are about real disasters and which one’s aren’t. You’ll have access to a dataset of 10,000 tweets that were hand classified. If this is your first time working on an NLP problem, we've created a quick tutorial to get you up and running.

The objective of this challenge is the creation, curation and collation of good quality African language datasets for a specific NLP task. This task-specific NLP dataset will serve as the downstream task we can evaluate future language models on.

(Algorithm development) Create an algorithm to detect a rectangular area including objects from the image of vehicle front camera. (Algorithm implementation) Design hardware accelerators and implement algorithm on the target FPGA board.

Identify videos with facial or voice manipulations

The objective of this challenge is to interpret and generate insights from the data on the 2015 flooding and its aftermath specifically in southern Malawi.

Predict flood extent caused by storms in southern Malawi.