Online learning - Apache Spark alternatives: Vowpal Wabbit. (18.06.2015)
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Slides about Spark alternatives in online learning. Alexander Krot presented Vowpal Wabbit on Big Data Developers in Moscow meetup.
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With over one billion views in a year, it’s no wonder that so many speakers are looking to TED for ideas on how to share their message more effectively.
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Whether you are gearing up to get on a TED stage yourself, or just want to master the skills that so many of their speakers possess, these tips and quotes from Chris Anderson, the TED Talks Curator, will encourage you to make the most impactful impression on your audience.
See the full article and more summaries like this on SpeakerHub here: https://speakerhub.com/blog/5-presentation-tips-ted-gives-its-speakers
See the original article on Forbes here:
http://www.forbes.com/forbes/welcome/?toURL=http://www.forbes.com/sites/carminegallo/2016/05/06/5-public-speaking-tips-ted-gives-its-speakers/&refURL=&referrer=#5c07a8221d9b
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Online learning - Apache Spark alternatives: Vowpal Wabbit. (18.06.2015)
- 1. Онлайн обучение альтернатива Apache Spark Крот Александр Москва, 2015
- 2. • Large Scale Machine Learning • MapReduce подход • Онлайн обучение – Классификация и регрессия с помощью линейных функций – Процесс обновления весов • Преимущества онлайн обучения • Выбор функции потерь • Метрики качества • Обзор Vowpal Wabbit План Москва, 2015 2
- 3. • Пусть дана большая обучающая выборка объектов – Много объектов – Большая размерность признакового описания • Фичи при этом уже подготовлены – Не нужно делать сложный ETL • Необходимо построить классификатор или решить задачу регрессии Такое пока что бывает очень редко! Large Scale Machine Learning Москва, 2015 3
- 5. Первая сумма MapReduce подход Москва, 2015 5
- 6. Вторая сумма MapReduce подход Москва, 2015 6
- 7. • Пусть дана обучающая выборка объектов • Необходимо решить задачу регрессии: – Найти такой вектор весов W, что значение целевой переменной объекта y близка к (W,x) • Необходимо обучить классикатор: – Найти такой вектор весов W, что метка класса объекта y близка к sigmoid( (W,x) ) – в случае логистической регрессии Онлайн обучение Москва, 2015 7
- 8. • Инициализируем начальные веса (обычно W = 0) • Далее, итерационно: – Получаем признаки очередного объекта x – Прогнозируем целевую переменную (метку или значение) y для этого объекта – Обновляем веса так, чтобы y был близок к своему истинному значению – Как правило используются модификации метода стохастического градиента Процесс Москва, 2015 8
- 9. В зависимости от того, что прогнозируем • Наиболее часто встречающее значение – Квантильная с параметром 0.5 • Прибыль, продажи, нагрузка – Квадратичная • Вероятность дефолта, клика – Логистическая Выбор функции потерь Москва, 2015 9
- 10. • Используется Progressive Validation – Получаем признаки объекта x – Делаем линейное предсказание y – Измеряем ошибку на текущем объекте – Обновляем веса, чтобы уменьшить ошибку на текущем объекте • Метрика качества в момент T – средняя ошибка на первых T объектах Метрики качества Москва, 2015 10
- 11. • Разработан в Microsoft/Yahoo Research • Работает из командной строки • Имеет собственный формат данных – Label |A feature1:value1 |B feature2:value2 • Легко делается множество эвристик – Квадратичные, кубичные фичи, n-граммы (-q, -cubic, - ngram) – L1, L2 – нормализация – Learning Rate Обзор Vowpal Wabbit Москва, 2015 11
- 12. • Решение задачи Титаник – Преобразуем фичи (оставляем как есть) – Обучаем классификатор: vw train_titanic.vw -f model.vw --binary --passes 20 -c -q ff --normalized --l1 0.00000001 --l2 0.0000001 -b 24 – Делаем predict на тестовой выборке: vw -d test_titanic.vw -t -i model.vw -p preds_titanic.txt – Получаем 0.79 AUC Обзор Vowpal Wabbit Москва, 2015 12
- 13. Спасибо за внимание! MLClass.ru [email protected] Москва, 2015