​Fruit identification using Arduino and TensorFlow

By Dominic Pajak and Sandeep Mistry : https://blog.arduino.cc/2019/11/07/fruit-identification-using-arduino-and-tensorflow/

#Arduino #TensorFlow #DeepLearning

🔗 Fruit identification using Arduino and TensorFlow
By Dominic Pajak and Sandeep Mistry Arduino is on a mission to make machine learning easy enough for anyone to use. The other week we announced the availability of TensorFlow Lite Micro in the Arduino Library Manager. With this, some cool ready-made ML examples such as speech recognition, simple

​Kaggle Livecoding: Data cleaning!🧹 | Kaggle

🔗 Kaggle Livecoding: Data cleaning!🧹 | Kaggle
This week it's all about the data cleaning. We'll be taking a raw survey dataset & get it ready to be used for classification. About Kaggle: Kaggle is the world's largest community of data scientists. Join us to compete, collaborate, learn, and do your data science work. Kaggle's platform is the fastest way to get started on a new data science project. Spin up a Jupyter notebook with a single click. Build with our huge repository of free code and data. Stumped? Ask the friendly Kaggle community for help.

​Machine Learning and Data Analysis — Inha University (Part-2)

🔗 Machine Learning and Data Analysis — Inha University (Part-2)
Chapter-2: Python Data Structure — Data Type

​Best Practices for NLP Classification in TensorFlow 2.0

🔗 Best Practices for NLP Classification in TensorFlow 2.0
Use Data Pipelines, Transfer Learning and BERT to achieve 85% accuracy in Sentiment Analysis

​OpenMemDB: A Wait-Free, In-Memory Database

🔗 OpenMemDB: A Wait-Free, In-Memory Database

🎥 The Evolution of Deep Learning Symposium featuring a research talk by Radford Neal
👁 1 раз ⏳ 3021 сек.

​Ken Burns Effect, Now In 3D!

🔗 Ken Burns Effect, Now In 3D!
❤️ Pick up cool perks on our Patreon page: https://www.patreon.com/TwoMinutePapers 📝 The paper "3D Ken Burns Effect from a Single Image" is available here: https://arxiv.org/abs/1909.05483 The paper with the Microplanet scene at the start is available here: https://users.cg.tuwien.ac.at/zsolnai/gfx/gaussian-material-synthesis/ Image credits: Ian D. Keating, Kirk Lougheed (Link: https://www.flickr.com/photos/kirklougheed/36766944501 ), Leif Skandsen, Oliver Wang, Ben Abel, Aurel Manea, Jocelyn Erskine-Kel

​HoloGAN (A new generative model) learns 3D representation from natural images

Paper: https://arxiv.org/pdf/1904.01326.pdf
Github: https://github.com/thunguyenphuoc/HoloGAN/
Dataset: https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html
https://www.marktechpost.com/2019/11/04/hologan-a-new-generative-model-learns-3d-representation-from-natural-images/

🔗 thunguyenphuoc/HoloGAN
HoloGAN. Contribute to thunguyenphuoc/HoloGAN development by creating an account on GitHub.

​Большое интервью про Big Data: зачем за нами следят в соцсетях и кто продает наши данные?
Disclaimer. Специалист по Big Data, Артур Хачуян, рассказал, как соцсети могут читать наши сообщения, как наш телефон нас подслушивает, и кому все это нужно. Эта статья — расшифровка большого интервью. Есть люди, которые экономят время и любят текст, есть те, кто не может на работе или в дороге смотреть видео, но с радостью читает Хабр, есть слабослышащие, для которых звуковая дорожка недоступна или сложна для восприятия. Мы решили для всех них и вас расшифровать отличный контент. Кто всё же предпочитает видео — ссылка в конце.

Каждый день мы что-то пишем, разыскиваем и выкладываем в интернете, и каждый день кто-то следит за нами по ту сторону экрана. Специальные программы сканируют фото, лайки и тексты, чтобы продать наши данные рекламным компаниям или полиции. Можно назвать это паранойей или научной фантастикой, но телефон, круг общения, переписка или ориентация — больше не секрет.

🔗 Большое интервью про Big Data: зачем за нами следят в соцсетях и кто продает наши данные?
Disclaimer. Специалист по Big Data, Артур Хачуян, рассказал, как соцсети могут читать наши сообщения, как наш телефон нас подслушивает, и кому все это нужно. Эта...

​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:
https://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 https://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.