Тренировки ML

In this competition, your goal is to predict short and long answer responses to real questions about Wikipedia articles. The dataset is provided by Google's Natural Questions, but contains its own unique private test set. A visualization of examples shows long and—where available—short answers. In addition to prizes for the top teams, there is a special set of awards for using TensorFlow 2.0 APIs.

Leverage millions of images of animals on the Serengeti to build a classifier that distinguishes between gazelles, lions, and more!

PBS KIDS, a trusted name in early childhood education for decades, aims to gain insights into how media can help children learn important skills for success in school and life. In this challenge, you’ll use anonymous gameplay data, including knowledge of videos watched and games played, from the PBS KIDS Measure Up! app, a game-based learning tool developed as a part of the CPB-PBS Ready To Learn Initiative with funding from the U.S. Department of Education. Competitors will be challenged to predict scores on in-game assessments and create an algorithm that will lead to better-designed games and improved learning outcomes. Your solutions will aid in discovering important relationships between engagement with high-quality educational media and learning processes.

Online Hackathon

The objective of this challenge is to build a machine learning model that accurately predicts when and where the next road incident will occur in Cape Town, South Africa. Data scientists will build their models on historic road incident data as well as traffic pattern data from Uber.

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.

Первый масштабный хакатон в России по теме цифровизации индустрии туризма. Организатор хакатона, Комитет по туризму города Москвы, и партнеры предлагают десять разных задач, связанных со сферой путешествий. Вместе мы хотим найти нестандартные технологические решения, познакомиться с новыми командами на этом рынке и создать новые проекты в области туризма и гостеприимства.

In this competition, you’re challenged to use this new dataset to build predictive algorithms for different subjective aspects of question-answering. The question-answer pairs were gathered from nearly 70 different websites, in a "common-sense" fashion. Our raters received minimal guidance and training, and relied largely on their subjective interpretation of the prompts. As such, each prompt was crafted in the most intuitive fashion so that raters could simply use their common-sense to complete the task. By lessening our dependency on complicated and opaque rating guidelines, we hope to increase the re-use value of this data set. What you see is what you get!

Higher School of Economics and Yandex are proud to announce the 3rd international data analysts olympiad. This will be a team machine learning competition, divided into two stages. The first stage will be online, open to all participants. The second stage will be the offline on-site finals, in which the top 30 performing teams from the online round will compete at the Yandex office in Moscow. The first track will be a traditional data science competition. Having a labeled training data set, participants will be asked to make a prediction for the test data and submit their predictions to the leaderboard. In this track, participants can produce arbitrarily complex models. If you like to use 4-level stacking or deep neural networks, this is the right track for you – you will only need to submit test predictions. However, those who qualify for the finals will be asked to submit the full code of the solution for validation by the judges.

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.

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

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.

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.

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.

Identify videos with facial or voice manipulations

(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.

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.