​A Beginner’s Guide on Machine Learning Model Monitoring

🔗 A Beginner’s Guide on Machine Learning Model Monitoring
Understand how to manage your machine learning models after deployment

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🎥 Coronavirus Outbreak Prediction Using Machine Learning | April Update | Simplilearn
👁 1 раз ⏳ 3690 сек.
Predictions for March 2020: https://www.youtube.com/watch?v=sHWKN5dakPw
The outbreak of Coronavirus has taken the world by storm. It has caused a ...

🎥 Data Science Live - 3 | Machine Learning Algorithms | ML Tutorial | Data Science Training | Edureka
👁 1 раз ⏳ 2596 сек.
🔥 Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification
This Machine Learning Algorithms Tutorial shall teach you what machine learning is, and the various ways in which you can use machine learning to solve a problem! Towards the end, you will learn how to prepare a data-set for model creation and validation and how you can create a model using any machine learning algorithm!

Check our complete Data Science playlist here: https://bit.ly/2KEEFdf
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🎥 Machine Learning: Deep Learning Hands-On with TensorFlow
👁 1 раз ⏳ 3927 сек.
Hands-on coding with explanations: how to classify the breast cancer Wisconsin Dataset from Scikit-Learn with TensorFlow. Based on a JupyterLab (Jupyter Notebook) with Python 3.


Building a neural network with one hidden layer (8 nodes, ReLU activation) and one output node (Sigmoid activation).


As an intro after the previous theory module, network architectures are explained using the TensorFlow Playground.



Part of a Machine Learning & Deep Learning lecture series for Digital Healthcare at the St. Pöl

​Difference Between Algorithm and Model in Machine Learning - Machine Learning Mastery

🔗 Difference Between Algorithm and Model in Machine Learning - Machine Learning Mastery
Machine learning involves the use of machine learning algorithms and models. For beginners, this is very confusing as often

​Sentence / Word Tokenizer for Russian

https://github.com/natasha/razdel#sentencies
https://github.com/natasha/razdel#tokens

🔗 natasha/razdel
Rule-based tokenizer, sentenizer for russian language - natasha/razdel

​Let’s train on millions and billions of images!

🔗 Let’s train on millions and billions of images!
Exploring the state of art techniques that leverage the power of massive amounts of unlabeled data

​ИИ: Дополняя завтрашний мир

🔗 ИИ: Дополняя завтрашний мир
Объем данных, генерируемых каждый день, намного превосходит возможности человеческого восприятия. И если мы планируем использовать этот объем более-менее осмысле...

​Учим нейросети в Google Таблицах

🔗 Учим нейросети в Google Таблицах
Хочу с вами зачелленджить одну интересную штуку: попробовать обучить нейросеть в Google Таблицах. Безо всяких макросов и прочих хаков, на чистых формулах. Задач...

​Небольшое сравнение функциональности Azure DataFactory и IBM DataStage

🔗 Небольшое сравнение функциональности Azure DataFactory и IBM DataStage
В течение некоторого времени я тестировал Microsoft Azure DataFactory, чтобы сравнить его функциональность (пока не производительность) с существующим у моего кл...

​Relative importance analysis: A better way to communicate multiple regression results

🔗 Relative importance analysis: A better way to communicate multiple regression results
Many industry clients are concerned about the relative importance of factors (X variables) that drive an outcome (Y). What are the…

​Applying Machine Learning to…..Yeast?">
Applying Machine Learning to…..Yeast?

🔗 Applying Machine Learning to…..Yeast?
Posted by Ted Baltz, Senior Staff Software Engineer, Google Research, Accelerated Science Team Humans have a long history with yeast, ti...

​Single Stage Instance Segmentation — A Review

🔗 Single Stage Instance Segmentation — A Review
A glimpse into the future of real-time instance segmentation

🎥 Machine Learning - 2.2.1 - Multiple features of one input
👁 1 раз ⏳ 1103 сек.
PLAYLIST: https://tinyurl.com/Machine-Learning-PL
Notes: https://tinyurl.com/Machine-Learning-Nts
See playlist description for topic headings

🎥 Daniel Hindrikes - Building smarter apps powered by Machine Learning
👁 1 раз ⏳ 5896 сек.
Welcome to a Knowledge Sharing Wednesday Stream with tretton37.

Today's speaker is Daniel Hindrikes, App Innovation Architect, Microsoft MVP and co-author of the popular book, Xamarin.Forms Projects.

Building smarter apps powered by Machine Learning.

What if your apps could solve problems, deduce information from pictures and solve complex tasks? In the future, mobile apps will be so much more than a data displaying device. Using AI, apps will be able to answer complex problems at a whole new level!

In

🎥 Искусственный интеллект VS COVID-19. Лекция профессора Сколтеха Максима Федорова
👁 1 раз ⏳ 4514 сек.
«Сколтех в Точке кипения». Совместный научно-просветительский проект Сколковского института науки и технологий и пространства коллективной работы "...

​A state-of-the-art open source chatbot

🔗 A state-of-the-art open source chatbot
Today we’re announcing that Facebook AI has built and open-sourced Blender, the largest-ever open-domain chatbot. It outperforms others in terms of engagement and also feels more human, according to human evaluators.

Опубликовал очередной обзор ( https://www.youtube.com/watch?v=4RNBDDui6Yw ) пре релиза dplyr 1.0.0, официальный релиз которого ожидается уже в мае.

В этом видео рассмотрены изменения которые коснулись функции summarise().

В основе обзора лежит статья Хедли Викхема - https://www.tidyverse.org/blog/2020/03/dplyr-1-0-0-summarise/

https://www.youtube.com/watch?v=4RNBDDui6Yw

🎥 Обзор релиза dplyr 1.0.0: обновлённая функция summarise()
👁 1 раз ⏳ 798 сек.
Ещё одно видео в основе которого лежит одна из серии статей Хедли Викхема предшествующей релизу dplyr 1.0.0.

Данное видео демонстрирует новые возможности функции summarise().

Код используемый в статье: https://github.com/selesnow/publications/blob/master/code_example/dplyr-1-0-0/dplyr_1.0.0_-_new_summarise.R
Оригинальная статья Викхема: https://www.tidyverse.org/blog/2020/03/dplyr-1-0-0-summarise/

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​TensorTrade
An open source reinforcement learning framework for training, evaluating, and deploying robust trading agents: https://github.com/tensortrade-org/tensortrade

🔗 tensortrade-org/tensortrade
An open source reinforcement learning framework for training, evaluating, and deploying robust trading agents. - tensortrade-org/tensortrade