​Cost Function Explained in less than 5 minutes

🔗 Cost Function Explained in less than 5 minutes
One of the most important Machine Learning Concepts explained in less than 5 minutes…

​Wine Classifier Using Supervised Learning with 98% Accuracy

🔗 Wine Classifier Using Supervised Learning with 98% Accuracy
Guide to supervised learning

​Live object detection of sea otters (because why not?)

🔗 Live object detection of sea otters (because why not?)
Do you know about sea otters? Y’know, the marine mammals that are super cute? Fun fact: they hold hands while they sleep so they don’t…

​Live object detection of sea otters (because why not?)

🔗 Live object detection of sea otters (because why not?)
Do you know about sea otters? Y’know, the marine mammals that are super cute? Fun fact: they hold hands while they sleep so they don’t…

​- Quantum Complexity of Time Evolution with Chaotic Hamiltonians.
(arXiv:1905.05765v3 [hep-th] UPDATED)
http://arxiv.org/abs/1905.05765

🔗 Quantum Complexity of Time Evolution with Chaotic Hamiltonians
We study the quantum complexity of time evolution in large-$N$ chaotic systems, with the SYK model as our main example. This complexity is expected to increase linearly for exponential time prior to saturating at its maximum value, and is related to the length of minimal geodesics on the manifold of unitary operators that act on Hilbert space. Using the Euler-Arnold formalism, we demonstrate that there is always a geodesic between the identity and the time evolution operator $e^{-iHt}$ whose length grows linearly with time. This geodesic is minimal until there is an obstruction to its minimality, after which it can fail to be a minimum either locally or globally. We identify a criterion - the Eigenstate Complexity Hypothesis (ECH) - which bounds the overlap between off-diagonal energy eigenstate projectors and the $k$-local operators of the theory, and use it to show that the linear geodesic will at least be a local minimum for exponential time. We show numerically that the large

🎥 Почему видеокарта и процессор не могут заменить друг друга
👁 1 раз ⏳ 832 сек.
Комплектующие - https://www.e-katalog.ru/u/v9p6UC/a
Процессоры - https://www.e-katalog.ru/u/DkzaI7/a
В видео разбираемся с вопросом о том почему процессор и видеокарта не взаимозаменяемые комплектующие и почему существуют и процессор и видеокарта и почему их нельзя объединить в одно устройство.
https://pc-01.tech/CPU_GPU/ - текстовая версия

https://pc-01.tech - сайт канала. Свежие новости о железе, обзоры и тесты комплектующих.

https://vk.com/pc_0_1 - группа "Этот компьютер" - свежие и актуальные новости

​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

🔗 Открыть в Telegram



📝 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