​В математике часто требуется хорошая карта, чтобы найти ответы

Математики пытаются выяснить, когда проблемы могут быть решены с использованием имеющихся знаний - и когда вместо этого они должны наметить новый путь

https://www.quantamagazine.org/in-math-it-often-takes-a-good-map-to-find-answers-20200601/

🔗 In Mathematics, It Often Takes a Good Map to Find Answers
Mathematicians try to figure out when problems can be solved using current knowledge — and when they have to chart a new path instead.

​Machine Learning A Bayesian and Optimization Perspective
Наш телеграм канал - tglink.me/ai_machinelearning_big_data

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📝 machine_learning_bayesian_optimization_perspective_2nd@NetworkArtificial (1).pdf - 💾23 947 799

​The relationship between Perplexity and Entropy in NLP

🔗 The relationship between Perplexity and Entropy in NLP
Use Information Theory to understand NLP Metrics

​The relationship between Perplexity and Entropy in NLP

🔗 The relationship between Perplexity and Entropy in NLP
Use Information Theory to understand NLP Metrics

​Как коронавирус повлиял на ML-проекты Такси, Еды и Лавки. Доклад Яндекса

🔗 Как коронавирус повлиял на ML-проекты Такси, Еды и Лавки. Доклад Яндекса
Меня зовут Илья Ирхин, я руководитель отдела машинного обучения и анализа данных Яндекс.Такси. Коронавирус и самоизоляция, безусловно, повлияли на наши ML-прое...

​New tool automatically turns math into pictures: Visualizations poised to enrich teaching, scientifi

🔗 New tool automatically turns math into pictures: Visualizations poised to enrich teaching, scientifi
Some people look at an equation and see a bunch of numbers and symbols; others see beauty. Thanks to a new tool, anyone can now translate the abstractions of mathematics into beautiful and instructive illustrations. The tool enables users to create diagrams simply by typing an ordinary mathematical expression and letting the software do the drawing.

​Abstractive Text Summarization with NLP

🔗 Abstractive Text Summarization with NLP
RNNs, LSTMs, and Word Embeddings For Text Summarization

​Создание детектора социального дистанцирования

В этом туториале вы узнаете, как реализовать детектор социального дистанцирования COVID-19 с использованием OpenCV, глубокого обучения и компьютерного зрения.

https://www.pyimagesearch.com/2020/06/01/opencv-social-distancing-detector/

🔗 OpenCV Social Distancing Detector - PyImageSearch
In this tutorial, you will learn how to implement a COVID-19 social distancing detector using OpenCV, Deep Learning, and Computer Vision.

​Proposing a new effect of learning rate decay — Network Stability

🔗 Proposing a new effect of learning rate decay — Network Stability
Uncovering learning rate as a form of regularisation in stochastic gradient descent

​How to create a C++ project using Ceres Solver?

🔗 How to create a C++ project using Ceres Solver?
Step-by-step procedure to get started with Ceres Solver

​Create your own Word Cloud

🔗 Create your own Word Cloud
Learn to build a very simple word cloud using Python using only a few lines of code!

🎥 Classifying sound using Machine Learning (Artificial Summit February 2020 @ KnowIt)
👁 1 раз ⏳ 3433 сек.
This is a repost of https://www.youtube.com/watch?v=1H63PewtDbo

Presentation slides and notes available at https://github.com/jonnor/machinehearing#classifying-sound-using-machine-learning

​How to Perform Feature Selection for Regression Data - Machine Learning Mastery

🔗 How to Perform Feature Selection for Regression Data - Machine Learning Mastery
Feature selection is the process of identifying and selecting a subset of input variables that are most relevant to the target variable. Perhaps the simplest case of feature selection is the case where there are numerical input variables and a numerical target for regression predictive modeling. This is because the strength of the relationship between each input variable and the target

​How to Perform Feature Selection for Regression Data - Machine Learning Mastery

🔗 How to Perform Feature Selection for Regression Data - Machine Learning Mastery
Feature selection is the process of identifying and selecting a subset of input variables that are most relevant to the target variable. Perhaps the simplest case of feature selection is the case where there are numerical input variables and a numerical target for regression predictive modeling. This is because the strength of the relationship between each input variable and the target

​Learn Algorithmic Trading Build and deploy algorithmic trading systems
Наш телеграм канал - tglink.me/ai_machinelearning_big_data

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📝 Learn Algorithmic Trading Build and deploy algorithmic trading systems and strategies using Python and advanced data analysis.. - 💾16 700 530

​How to Deploy TensorFlow Models to the Web

🔗 How to Deploy TensorFlow Models to the Web
Using basic JavaScript to run Python-built models in the browser

​How to Deploy TensorFlow Models to the Web

🔗 How to Deploy TensorFlow Models to the Web
Using basic JavaScript to run Python-built models in the browser

​A Scalable and Cloud-Native Hyperparameter Tuning System

Katib is a Kubernetes-based system for Hyperparameter Tuning and Neural Architecture Search. Katib supports a number of ML frameworks, including TensorFlow, Apache MXNet, PyTorch, XGBoost, and others.

Github: https://github.com/kubeflow/katib

Getting started with Katib: https://www.kubeflow.org/docs/components/hyperparameter-tuning/hyperparameter/

Paper: https://arxiv.org/abs/2006.02085v1
Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 kubeflow/katib
Repository for hyperparameter tuning. Contribute to kubeflow/katib development by creating an account on GitHub.