​r/HongKong - Inspired by the protests, I made a cap that blocks facial recognition when used. Plans

🔗 r/HongKong - Inspired by the protests, I made a cap that blocks facial recognition when used. Plans
41,280 votes and 641 comments so far on Reddit

​Classification of Histopathology Images with Deep Learning: A Practical Guide

🔗 Classification of Histopathology Images with Deep Learning: A Practical Guide
Everything you need to know to train your own deep learning classifier for histopathology images.

🎥 Юрий Бабуров: "Рассказ про наш открытый корпус русской речи" 2019-10-31
👁 1 раз ⏳ 3147 сек.
Рассказ про наш открытый корпус русской речи для распознавания и синтеза. Путь к успеху длиной в 10 месяцев. Митап в ЦФТ.

​Generate Modern Stylish Wordcloud with stylecloud

🔗 Generate Modern Stylish Wordcloud with stylecloud
But deep down, all of us have always wished for modern-stylish-beautiful wordclouds. That wish has become true with this new python…

​ICCV 2019 Best Paper Award (Marr Prize): SinGAN: Learning a Generative Model from a Single Natural Image https://arxiv.org/abs/1905.01164

🔗 SinGAN: Learning a Generative Model from a Single Natural Image
We introduce SinGAN, an unconditional generative model that can be learned from a single natural image. Our model is trained to capture the internal distribution of patches within the image, and is then able to generate high quality, diverse samples that carry the same visual content as the image. SinGAN contains a pyramid of fully convolutional GANs, each responsible for learning the patch distribution at a different scale of the image. This allows generating new samples of arbitrary size and aspect ratio, that have significant variability, yet maintain both the global structure and the fine textures of the training image. In contrast to previous single image GAN schemes, our approach is not limited to texture images, and is not conditional (i.e. it generates samples from noise). User studies confirm that the generated samples are commonly confused to be real images. We illustrate the utility of SinGAN in a wide range of image manipulation tasks.

​How To Use Deep Learning Even with Small Data

🔗 How To Use Deep Learning Even with Small Data
And why it is so important

​Animating gAnime with StyleGAN: The Tool

🔗 Animating gAnime with StyleGAN: The Tool
In-depth tutorial for an open-source GAN research tool

🎥 Machine Learning for Cyber Security: Datasets and Features
👁 1 раз ⏳ 7757 сек.
Description: In this video, we are going to talk about datasets and features.

You can also visit our website here:
http://www.ricardocalix.com/teaching/MLCyber/course1.htm

Machine Learning for Cyber Security Professionals -- Prof. Calix
Purdue University Northwest, Hammond, IN, USA

Director and lecturer: Dr. Ricardo A. Calix, PhD
Lectures and labs creator: Tingyu Chen
Slides editor and accessibility staff: Feihong Liu
Filming and Video editor: Dingkai Zhang
All of above were involved in the recording of

🎥 Deep Reinforcement Learning in the Real World -Sergey Levine
👁 1 раз ⏳ 2783 сек.
Workshop on New Directions in Reinforcement Learning and Control

Topic: Deep Reinforcement Learning in the Real World
Speaker: Sergey Levine
Affiliation: University of Berkeley
Date: November 8, 2019

For more video please visit http://video.ias.edu

🎥 Глубокое обучение для классификации ЭКГ
👁 4 раз ⏳ 4504 сек.
На сегодняшнем семинаре Ушенин Константин расскажет о подходах к классификации электрокардиограмм (ЭКГ), которые были предложены победителями PhysioNet Challenge 2017. Речь пойдет об общем устройстве данных для соревнования, а так же о двух принципиально разных подходах к классификации ЭКГ. Первый использует преобразование сигнала в спектрограмму и применяет сверточные нейронные сети. Второй основан на выделении признаков из сигнала классическими методами обработки электрокардиограмм и передает признаки в а

​Real time Shape Detection using Contours [9] | OpenCV Python Tutorials for Beginners 2019

https://youtu.be/Fchzk1lDt7Q

useful Links:

OpenCV Python Tutorial Playlist:
https://www.youtube.com/watch?v=CJXIj...

How to install Opencv in Python:
https://youtu.be/CJXIjApHYVs

Real time color Detection:
https://youtu.be/Tj4zEX_pdUg

5 Must Know OpencCV Functions:
https://youtu.be/7kHhz7nkpBw

🔗 Real time Shape Detection using Contours [9] | OpenCV Python Tutorials for Beginners 2019
In this video we will learn how to detect shapes of objects by finding their contours. Contours are basically outline that bound the shape or form of an object. So we will be detecting multiple shapes and how many corners points each shape has along with its area . Links : OpenCV Python Tutorial Playlist: https://www.youtube.com/watch?v=CJXIjApHYVs&list=PLMoSUbG1Q_r_sc0x7ndCsqdIkL7dwrmNF How to install Opencv in Python: https://youtu.be/CJXIjApHYVs Real time color Detection: https://youtu.be/Tj4zEX_p

​Deep Learning for Population Genetic Inference

🔗 Deep Learning for Population Genetic Inference
Author Summary Deep learning is an active area of research in machine learning which has been applied to various challenging problems in computer science over the past several years, breaking long-standing records of classification accuracy. Here, we apply deep learning to develop a novel likelihood-free inference framework to estimate population genetic parameters and learn informative features of DNA sequence data. As a concrete example, we focus on the challenging problem of jointly inferring natural selection and demographic history.

Gource visualization of DeepFaceLab https://github.com/iperov/DeepFaceLab

https://www.youtube.com/watch?v=w3IkfPmU2L0

🎥 Evolution of DeepFaceLab (Gource Visualization) [2019-11-04]
👁 1 раз ⏳ 177 сек.
Gource visualization of DeepFaceLab (https://github.com/iperov/DeepFaceLab) [2019-11-04]. DeepFaceLab is a tool that utilizes machine learning to replace faces in videos. Includes prebuilt ready to work standalone Windows 7,8,10 binary (look readme.md).

This visualization was generated with the following command:
gource \
--path path/to/repo \
--seconds-per-day 1 \
--title "DeepFaceLab" \
--date-format "%F" \
-1280x720 \
--file-idle-time 0 \
--auto-skip-seconds 0.75 \
--multi-sampling \
--stop-at-end \
--k

​Steps to basic modern NN model from scratch

Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 Steps to basic modern NN model from scratch
The blog will consist of the steps to create a primary neural network, starting from understanding matrix multiplication to building your…

Машинное обучение

Нейрон на JavaScript
Обучение нейрона на JavaScript
Визуализация результатов нейрона на JavaScript
Однослойный персептрон на JavaScript (1)
Обучение однослойного персептрона на JavaScript
Многослойный персептрон (пример на пальцах)
Нейронная сеть : многослойный персептрон Румельхарта на JavaScript

#MachineLearning

🎥 Нейрон на JavaScript
👁 55 раз ⏳ 996 сек.
В данном уроке рассматривается создание одного нейрона на JavaScript.
Ссылка на CodePen: https://codepen.io/raman-mamedov/pen/WmvERM?editors=0012

🎥 Обучение нейрона на JavaScript
👁 21 раз ⏳ 2243 сек.
В данном уроке рассматривается процесс обучение одного нейрона. Реализация данного процесса. осуществляется на языке JavaScript. Затрагивается полн...

🎥 Визуализация результатов нейрона на JavaScript
👁 12 раз ⏳ 1763 сек.
В этом уроке рассматривается процесс визуализации результатов одного нейрона, который решает задачу классификации сторон.

🎥 Однослойный персептрон на JavaScript (1)
👁 9 раз ⏳ 1667 сек.
В данном видео будет рассмотрен процесс работы однослойного персептрона, с учетом всех нюансов его функционирования. В результате будет создана схе...

🎥 Обучение однослойного персептрона на JavaScript
👁 7 раз ⏳ 1804 сек.
В данном видео рассматривается как обучить однослойный персептрон который мы разработали в прошлом уроке на языке JavaScript. Будет применен градие...

🎥 Многослойный персептрон (пример на пальцах)
👁 6 раз ⏳ 1249 сек.
Нейронные сети применяемые в современных технологиях, получили свое начало от такого вида персептрона как многослойный. Искусственный интеллект по...

🎥 Нейронная сеть : многослойный персептрон Румельхарта на JavaScript
👁 15 раз ⏳ 1959 сек.
В данном уроке рассматривается процесс реализации и обучения многослойного персептрона Румельхарта. Это первая нейронная сеть, которую мы создаем ...

​Designing neural networks through Neuroevolution

https://www.nature.com/articles/s42256-018-0006-z

🔗 Designing neural networks through neuroevolution
Deep neural networks have become very successful at certain machine learning tasks partly due to the widely adopted method of training called backpropagation. An alternative way to optimize neural networks is by using evolutionary algorithms, which, fuelled by the increase in computing power, offers a new range of capabilities and modes of learning.

​How to Identify Hotel Deals — Using Machine Learning

🔗 How to Identify Hotel Deals — Using Machine Learning
I used machine learning to predict hotel prices in Las Vegas. We will use the prediction to determine if we are being offered a deal or…

🎥 Машинное обучение, основанное на теории решёток
👁 6 раз ⏳ 4981 сек.
Вторая лекция для студентов УлГУ по машинному обучению «ВКФ-метод машинного обучения, основанного на теории решёток». Прочитана 23 октября 2019 года доктором физико-математических наук, доцентом РГГУ, старшим научным сотрудником Федерального Исследовательского Центра «Информатика и Управление» Российской Академии наук Дмитрием Вячеславовичем Виноградовым.

Первая лекция: https://youtu.be/MlYAIO7silc

​Top 5 Machine Learning Libraries in Python
Free Udemy course
https://www.udemy.com/share/101suEBkseeV9WRX4=/

🔗 Бесплатное учебное руководство по теме "Машинное обучение" — The Top 5 Machine Learning Libraries in Python
A Gentle Introduction to the Top Python Libraries used in Applied Machine Learning - Бесплатный курс

​DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
Zhang et al.: https://arxiv.org/abs/1911.00536
#ArtificialIntelligence #MachineLearning #Transformer

🔗 DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer). Trained on 147M conversation-like exchanges extracted from Reddit comment chains over a period spanning from 2005 through 2017, DialoGPT extends the Hugging Face PyTorch transformer to attain a performance close to human both in terms of automatic and human evaluation in single-turn dialogue settings. We show that conversational systems that leverage DialoGPT generate more relevant, contentful and context-consistent responses than strong baseline systems. The pre-trained model and training pipeline are publicly released to facilitate research into neural response generation and the development of more intelligent open-domain dialogue systems.