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.
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.
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.