🎥 Internet of Things for Everything: Equipment Maintenance Advisor
👁 1 раз ⏳ 1353 сек.
👁 1 раз ⏳ 1353 сек.
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
IBM Maximo APM Equipment Maintenance Assistant augments your asset maintenance program with machine learning techniques and AI tools. This powerful...
🎥 Training & Testing Deep reinforcement learning (DQN) Agent - Reinforcement Learning p.6
👁 1 раз ⏳ 2917 сек.
👁 1 раз ⏳ 2917 сек.
Welcome to part 2 of the deep Q-learning with Deep Q Networks (DQNs) tutorials. In the previous tutorial, we were working on our DQNAgent class, an...
Vk
Training & Testing Deep reinforcement learning (DQN) Agent - Reinforcement Learning p.6
Welcome to part 2 of the deep Q-learning with Deep Q Networks (DQNs) tutorials. In the previous tutorial, we were working on our DQNAgent class, an...
Saving £millions for the NHS with Pandas
🔗 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.
🔗 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
🔗 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.
🔗 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
🔗 Understanding Gradient Boosting Machines — using XGBoost and LightGBM parameters
A quick, practical introduction to GBMs so you can stop using them as black-boxes
🔗 Understanding Gradient Boosting Machines — using XGBoost and LightGBM parameters
A quick, practical introduction to GBMs so you can stop using them as black-boxes
Towards Data Science
Understanding Gradient Boosting Machines — using XGBoost and LightGBM parameters
A quick, practical introduction to GBMs so you can stop using them as black-boxes
🎥 Machine Learning Sentiment Analysis And Word Embeddings Python Keras Example
👁 1 раз ⏳ 695 сек.
👁 1 раз ⏳ 695 сек.
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://medium.com/@corymaklin
GitHub: https://github.com/corymaklin
Twitter: https://twitter.com/CoryMaklin
Linkedin: https://www.linkedin.com/in/cory-makl...
Facebook: https://www.facebook.com/cory.maklin
Patreon: https://www.patreon.com/corymaklin
Vk
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
🔗 An introduction to high-dimensional hyper-parameter tuning
Best practices for optimizing ML models
🔗 An introduction to high-dimensional hyper-parameter tuning
Best practices for optimizing ML models
Medium
An introduction to high-dimensional hyper-parameter tuning
Best practices for optimizing ML models
🎥 Распознавание человека по лицу | Проекты по нейросетям
👁 4644 раз ⏳ 871 сек.
👁 4644 раз ⏳ 871 сек.
Проект распознавания и верификации человека по лицу с помощью глубоких нейронных сетей.
Проекты по глубоким нейронным сетям - https://goo.gl/Bqnpc...
Vk
Распознавание человека по лицу | Проекты по нейросетям
Проект распознавания и верификации человека по лицу с помощью глубоких нейронных сетей. Проекты по глубоким нейронным сетям - https://goo.gl/Bqnpc...
🎥 13 - ML & CV. Семинар: Строим первую нейронную сеть
👁 1 раз ⏳ 981 сек.
👁 1 раз ⏳ 981 сек.
Лектор: Игорь Слинько
Код доступен в репозитории курса https://github.com/SlinkoIgor/Neural_Networks_and_CV/blob/master/module03_sin_prediction.ip...
Vk
13 - ML & CV. Семинар: Строим первую нейронную сеть
Лектор: Игорь Слинько Код доступен в репозитории курса https://github.com/SlinkoIgor/Neural_Networks_and_CV/blob/master/module03_sin_prediction.ip...
Demystifying Tensorflow Time Series: Local Linear Trend
🔗 Demystifying Tensorflow Time Series: Local Linear Trend
Learn how Tensorflow uses linear dynamical system, Kalman filter and variational inference to model time series and make predictions.
🔗 Demystifying Tensorflow Time Series: Local Linear Trend
Learn how Tensorflow uses linear dynamical system, Kalman filter and variational inference to model time series and make predictions.
Towards Data Science
Demystifying Tensorflow Time Series: Local Linear Trend
Learn how Tensorflow uses linear dynamical system, Kalman filter and variational inference to model time series and make predictions.
Unsupervised State Representation Learning in Atari
🔗 Unsupervised State Representation Learning in Atari
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.
🔗 Unsupervised State Representation Learning in Atari
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.
🎥 PyData-трек
👁 6 раз ⏳ 14018 сек.
👁 6 раз ⏳ 14018 сек.
___________________________
ТЕХНОСТРИМ - образовательный канал для IT специалистов.
___________________________
ПОДПИСЫВАЙСЯ, ЕСЛИ ТЕБЕ ИНТЕРЕСНО...
Vk
PyData-трек
___________________________ ТЕХНОСТРИМ - образовательный канал для IT специалистов. ___________________________ ПОДПИСЫВАЙСЯ, ЕСЛИ ТЕБЕ ИНТЕРЕСНО...
🎥 Explaining AI: Putting Theory into Practice | Data Council SF '19
👁 1 раз ⏳ 2471 сек.
👁 1 раз ⏳ 2471 сек.
Download Slides: https://www.datacouncil.ai/talks/building-explainable-ai
WANT TO EXPERIENCE A TALK LIKE THIS LIVE?
Barcelona: https://www.dataco...
Vk
Explaining AI: Putting Theory into Practice | Data Council SF '19
Download Slides: https://www.datacouncil.ai/talks/building-explainable-ai WANT TO EXPERIENCE A TALK LIKE THIS LIVE? Barcelona: https://www.dataco...
Stand Up for Best Practices:
🔗 Stand Up for Best Practices:
Misuse of Deep Learning in Nature’s Earthquake Aftershock Paper
🔗 Stand Up for Best Practices:
Misuse of Deep Learning in Nature’s Earthquake Aftershock Paper
Towards Data Science
Stand Up for Best Practices:
Misuse of Deep Learning in Nature’s Earthquake Aftershock Paper
Benchmarking Python Distributed AI Backends with Wordbatch
🔗 Benchmarking Python Distributed AI Backends with Wordbatch
A comparison of the three major backend schedulers: Spark, Dask and Ray
🔗 Benchmarking Python Distributed AI Backends with Wordbatch
A comparison of the three major backend schedulers: Spark, Dask and Ray
Towards Data Science
Benchmarking Python Distributed AI Backends with Wordbatch
A comparison of the three major backend schedulers: Spark, Dask and Ray
Artificial Intelligence in Supply Chain Management: Predictive Analytics for Demand Forecasting
🔗 Artificial Intelligence in Supply Chain Management: Predictive Analytics for Demand Forecasting
Utilizing data to drive operational performance
🔗 Artificial Intelligence in Supply Chain Management: Predictive Analytics for Demand Forecasting
Utilizing data to drive operational performance
Towards Data Science
Artificial Intelligence in Supply Chain Management
Utilizing data to drive operational performance
🎥 Цвет, тип и толщина линии в matplotlib. Визуализация данных. Python для научной работы
👁 1 раз ⏳ 896 сек.
👁 1 раз ⏳ 896 сек.
Видеолекция по применению Python для визуализации данных. В этом видео разбираем как изменить стиль графика в matplotlib. Занятие будет полезно все...
Vk
Цвет, тип и толщина линии в matplotlib. Визуализация данных. Python для научной работы
Видеолекция по применению Python для визуализации данных. В этом видео разбираем как изменить стиль графика в matplotlib. Занятие будет полезно все...
Mixing Topology and Deep Learning with PersLay
🔗 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…
🔗 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…
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…
🎥 Deep Learning Tutorial with Python | Machine Learning with Neural Networks [Top Udemy Instructor]
👁 1 раз ⏳ 10210 сек.
👁 1 раз ⏳ 10210 сек.
In this video, Deep Learning Tutorial with Python | Machine Learning with Neural Networks Explained, Udemy instructor Frank Kane helps de-mystify t...
Vk
Deep Learning Tutorial with Python | Machine Learning with Neural Networks [Top Udemy Instructor]
In this video, Deep Learning Tutorial with Python | Machine Learning with Neural Networks Explained, Udemy instructor Frank Kane helps de-mystify t...
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.