Освобождаем руки нескольким аналитикам: API Livy для автоматизации типовых банковских задач
Наш телеграм канал - tglink.me/ai_machinelearning_big_data
Не секрет, что для оценки платежеспособности клиентов банки используют данные из различных источников (кредитное бюро, мобильные операторы и т.д.). Количество внешних партнёров может достигать нескольких десятков, а аналитиков в нашей команде наберётся лишь несколько человек. Возникает задача оптимизации работы небольшой команды и передачи рутинных задач вычислительным системам.
Как данные попадают в банк, и как команда аналитиков следит за этим процессом, разберём в данной статье.
https://habr.com/ru/company/homecredit/blog/457096/
🔗 Освобождаем руки нескольким аналитикам: API Livy для автоматизации типовых банковских задач
Привет, Хабр! Не секрет, что для оценки платежеспособности клиентов банки используют данные из различных источников (кредитное бюро, мобильные операторы и т.д.)...
Наш телеграм канал - tglink.me/ai_machinelearning_big_data
Не секрет, что для оценки платежеспособности клиентов банки используют данные из различных источников (кредитное бюро, мобильные операторы и т.д.). Количество внешних партнёров может достигать нескольких десятков, а аналитиков в нашей команде наберётся лишь несколько человек. Возникает задача оптимизации работы небольшой команды и передачи рутинных задач вычислительным системам.
Как данные попадают в банк, и как команда аналитиков следит за этим процессом, разберём в данной статье.
https://habr.com/ru/company/homecredit/blog/457096/
🔗 Освобождаем руки нескольким аналитикам: API Livy для автоматизации типовых банковских задач
Привет, Хабр! Не секрет, что для оценки платежеспособности клиентов банки используют данные из различных источников (кредитное бюро, мобильные операторы и т.д.)...
Хабр
Освобождаем руки нескольким аналитикам: API Livy для автоматизации типовых банковских задач
Привет, Хабр! Не секрет, что для оценки платежеспособности клиентов банки используют данные из различных источников (кредитное бюро, мобильные операторы и т.д.). Количество внешних партнёров...
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IBM Maximo APM Equipment Maintenance Assistant augments your asset maintenance program with machine learning techniques and AI tools. This powerful...Vk
Internet of Things for Everything: Equipment Maintenance Advisor
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Saving £millions for the NHS with Pandas
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Showing how the UK National Health Service can use the data analysis tool, Pandas, to save millions of pounds on pharmaceutical costs.
🔗 Saving £millions for the NHS with Pandas
Showing how the UK National Health Service can use the data analysis tool, Pandas, to save millions of pounds on pharmaceutical costs.
Towards Data Science
Saving £millions for the NHS with Pandas
Showing how the UK National Health Service can use the data analysis tool, Pandas, to save millions of pounds on pharmaceutical costs.
Using Artificial Intelligence Methods To Win In Poker
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Originally written in 2015, this articles reviews the state-of-the-art in poker research at the time & how BCI technology can influence it.
🔗 Using Artificial Intelligence Methods To Win In Poker
Originally written in 2015, this articles reviews the state-of-the-art in poker research at the time & how BCI technology can influence it.
Towards Data Science
Using Artificial Intelligence Methods To Win In Poker
Originally written in 2015, this articles reviews the state-of-the-art in poker research at the time & how BCI technology can influence it.
Understanding Gradient Boosting Machines — using XGBoost and LightGBM parameters
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A quick, practical introduction to GBMs so you can stop using them as black-boxes
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Towards Data Science
Understanding Gradient Boosting Machines — using XGBoost and LightGBM parameters
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Machine Learning Sentiment Analysis And Word Embeddings Python Keras Example
In this video, we cover word embeddings and how they relate to machine learning. Specifically, we walk through an example of how to implement sentiment analysis using word embeddings in Python.
CONNECT
Site: https://coryjmaklin.com/
Medium: https://medi…
CONNECT
Site: https://coryjmaklin.com/
Medium: https://medi…
An introduction to high-dimensional hyper-parameter tuning
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🔗 An introduction to high-dimensional hyper-parameter tuning
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Medium
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Проекты по глубоким нейронным сетям - https://goo.gl/Bqnpc...Vk
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Demystifying Tensorflow Time Series: Local Linear Trend
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Towards Data Science
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Unsupervised State Representation Learning in Atari
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State representation learning, or the ability to capture latent generative factors of an environment, is crucial for building intelligent agents that can perform a wide variety of tasks. Learning such representations without supervision from rewards is a challenging open problem. We introduce a method that learns state representations by maximizing mutual information across spatially and temporally distinct features of a neural encoder of the observations. We also introduce a new benchmark based on Atari 2600 games where we evaluate representations based on how well they capture the ground truth state variables. We believe this new framework for evaluating representation learning models will be crucial for future representation learning research. Finally, we compare our technique with other state-of-the-art generative and contrastive representation learning methods.
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State representation learning, or the ability to capture latent generative factors of an environment, is crucial for building intelligent agents that can perform a wide variety of tasks. Learning such representations without supervision from rewards is a challenging open problem. We introduce a method that learns state representations by maximizing mutual information across spatially and temporally distinct features of a neural encoder of the observations. We also introduce a new benchmark based on Atari 2600 games where we evaluate representations based on how well they capture the ground truth state variables. We believe this new framework for evaluating representation learning models will be crucial for future representation learning research. Finally, we compare our technique with other state-of-the-art generative and contrastive representation learning methods.
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ТЕХНОСТРИМ - образовательный канал для IT специалистов.
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ПОДПИСЫВАЙСЯ, ЕСЛИ ТЕБЕ ИНТЕРЕСНО...Vk
PyData-трек
___________________________ ТЕХНОСТРИМ - образовательный канал для IT специалистов. ___________________________ ПОДПИСЫВАЙСЯ, ЕСЛИ ТЕБЕ ИНТЕРЕСНО...
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WANT TO EXPERIENCE A TALK LIKE THIS LIVE?
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Explaining AI: Putting Theory into Practice | Data Council SF '19
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Stand Up for Best Practices:
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Misuse of Deep Learning in Nature’s Earthquake Aftershock Paper
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Towards Data Science
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Benchmarking Python Distributed AI Backends with Wordbatch
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Towards Data Science
Benchmarking Python Distributed AI Backends with Wordbatch
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Artificial Intelligence in Supply Chain Management: Predictive Analytics for Demand Forecasting
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Towards Data Science
Artificial Intelligence in Supply Chain Management
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Видеолекция по применению Python для визуализации данных. В этом видео разбираем как изменить стиль графика в matplotlib. Занятие будет полезно все...Vk
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In a former post, I presented Topological Data Analysis and its main descriptor, the so-called persistence diagram. In this post, I would…
Towards Data Science
Mixing Topology and Deep Learning with PersLay
In a former post, I presented Topological Data Analysis and its main descriptor, the so-called persistence diagram. In this post, I would…
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In this video, Deep Learning Tutorial with Python | Machine Learning with Neural Networks Explained, Udemy instructor Frank Kane helps de-mystify t...Vk
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Adapters: A Compact and Extensible Transfer Learning Method for NLP - Medium
🔗 Adapters: A Compact and Extensible Transfer Learning Method for NLP - Medium
Adapters obtain comparable results to BERT on several NLP tasks while achieving parameter efficiency.
🔗 Adapters: A Compact and Extensible Transfer Learning Method for NLP - Medium
Adapters obtain comparable results to BERT on several NLP tasks while achieving parameter efficiency.
Medium
Adapters: A Compact and Extensible Transfer Learning Method for NLP
Adapters obtain comparable results to BERT on several NLP tasks while achieving parameter efficiency.