В научном парке МГУ 15го декабря пройдет первый НГ Hack хакатон, посвященный NLP задачам. Участникам будут предложены 4 задания: классификация интентов, исправление орфографических ошибок, предсказание оценок в чатах, и обнаружение фальсификации телефонной статистики.
In this competition, you’ll develop accurate predictions of metered building energy usage in the following areas: chilled water, electric, natural gas, hot water, and steam meters. The data comes from over 1,000 buildings over a three-year timeframe.
In this challenge, you will be predicting roof type from drone imagery. The data consists of a set of overhead imagery of seven locations across three countries with labeled building footprints. Your goal is to classify each of the building footprints with the roof material type.
Your challenge is to characterize any differences in player movement between the playing surfaces and identify specific scenarios (e.g., field surface, weather, position, play type, etc.) that interact with player movement to present an elevated risk of injury. More details on the entry criteria are available in Evaluation Tab.
Можно ли узнать возраст клиента на основе информации о его расходах по карте? Мы подготовили задачу на базе реальных банковских транзакций. Совершенствуя свои продукты, банк использует информацию о пользователях, в том числе и возраст. Это помогает сделать персонализированные продукты, которые удовлетворяют реальные потребности клиентов. Но всегда ли календарный возраст соответствует образу жизни (и покупок) человека?
Santa needs the help of the Kaggle community to optimize which day each family is assigned to attend the workshop in order to minimize any extra expenses that would cut into next years toy budget! Can you help Santa out?
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.
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.
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.
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.
(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 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.