​Deconvolution and Checkerboard Artifacts

When we look very closely at images generated by neural networks, we often see a strange checkerboard pattern of artifacts. It’s more obvious in some cases than others, but a large fraction of recent models exhibit this behavior.

https://distill.pub/2016/deconv-checkerboard/

🔗 Deconvolution and Checkerboard Artifacts
When we look very closely at images generated by neural networks, we often see a strange checkerboard pattern of artifacts.

Deconvolution and Checkerboard Artifacts

When we look very closely at images generated by neural networks, we often see a strange checkerboard pattern of artifacts. It’s more obvious in some cases than others, but a large fraction of recent models exhibit this behavior.

https://distill.pub/2016/deconv-checkerboard/

The Science Behind AlphaStar
https://towardsdatascience.com/the-science-behind-alphastar-714bd7824d4b?source=collection_home---4------0---------------------

Review: STN — Spatial Transformer Network (Image Classification)
https://towardsdatascience.com/review-stn-spatial-transformer-network-image-classification-d3cbd98a70aa?source=collection_home---4------4---------------------

​Training Session on Designing Digital Solution: Overview of Machine Learning – Module 2

🔗 Training Session on Designing Digital Solution: Overview of Machine Learning – Module 2
Subscribe for more updates. Share with your geeky friends.

​Sentiment Analysis - Data Lit #1

🔗 Sentiment Analysis - Data Lit #1
Welcome to Data Lit! This 3 month course is an intro to data science for beginners. In this video, i'll explain how a popular data science technique called s...

​Clustering: K-means and Hierarchical

🔗 Clustering: K-means and Hierarchical
A friendly description of K-means clustering and hierarchical clustering with simple examples. No math is needed, only a visual mind and a will to learn.

https://habr.com/ru/post/437888/

Захват сигнала мышечной активности в систему машинного обучения

#machinelearning #neuralnets #deeplearning #машинноеобучение

Наш телеграмм канал - https://t.me/ai_machinelearning_big_data

https://habr.com/ru/post/437818/

Учим компьютер различать звуки: знакомство с конкурсом DCASE и сборка своего аудио классификатора за 30 минут

#machinelearning #neuralnets #deeplearning #машинноеобучение

Наш телеграмм канал - https://t.me/ai_machinelearning_big_data

​How to Lift the Veil Off Hidden Algorithms
In the absence of rules around algorithms, activists, lawyers, and tech workers are hacking transparency through other means

🔗 How to Lift the Veil Off Hidden Algorithms – Fast Company – Medium
In the absence of rules around algorithms, activists, lawyers, and tech workers are hacking transparency through other means

​🔗 Visualising Machine Learning Datasets with Google’s FACETS.
An open source tool from Google to easily learn patterns from large amounts of data

🎥 Machine Learning инженер в США | Что и где учить по машинному обучению
👁 82 раз ⏳ 1137 сек.
Михаил Ольховский – Machine Learning инженер в Postmates –  в этом видео рассказал о своем пути в программирование и поделился полезными онлайн ресурсами по изучению машинного обучение. Приятного просмотра!

Спасибо за просмотр и лайк!

Не забудьте подписаться на канал, чтобы не пропустить новые выпуски.

Запись на личную консультацию – pb@progblog.tv
-визовые вопросы,
-способы поиски работы в США,
-прохождение собеседований,
-составление резюме,
-заполнение LinkedIn-профиля,
-учеба в США по специальност

Everything you need to know about Scatter Plots for Data Visualisation
https://towardsdatascience.com/everything-you-need-to-know-about-scatter-plots-for-data-visualisation-924144c0bc5?source=collection_home---4------0---------------------

https://habr.com/ru/company/sibur_official/blog/437974/

Как победить в цифровом WorldSkills? На практическом примере

#machinelearning #neuralnets #deeplearning #машинноеобучение

Наш телеграмм канал - https://t.me/ai_machinelearning_big_data

​A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs

Paper https://arxiv.org/abs/1901.00945

#Neurons #Cognition #MachineLearning

🔗 A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrai
The visual system is hierarchically organized to process visual information in successive stages. Neural representations vary drastically across the first stages of visual processing: at the output of the retina, ganglion cell receptive fields (RFs) exhibit a clear antagonistic center-surround structure, whereas in the primary visual cortex, typical RFs are sharply tuned to a precise orientation. There is currently no unified theory explaining these differences in representations across layers. Here, using a deep convolutional neural network trained on image recognition as a model of the visual system, we show that such differences in representation can emerge as a direct consequence of different neural resource constraints on the retinal and cortical networks, and we find a single model from which both geometries spontaneously emerge at the appropriate stages of visual processing. The key constraint is a reduced number of neurons at the retinal output, consistent with the anatomy of the optic nerve as a stri

​Supervised vs Unsupervised vs Reinforcement Learning | Data Science Certification Training | Edureka

🔗 Supervised vs Unsupervised vs Reinforcement Learning | Data Science Certification Training | Edureka
(** Python Data Science Training: https://www.edureka.co/python **) In this video on Supervised vs Unsupervised vs Reinforcement learning, we’ll be discussin...

​Building a Predictive Model with R in SQL Server Machine Learning Services

🔗 Building a Predictive Model with R in SQL Server Machine Learning Services
Free trainings every Tuesday at 11am EST: http://pragmaticworks.com/Training/Courses#type=Free This webinar will discuss R and Python integration with SQL Se...

​Начинаем работу с Azure Machine Learning service

Сегодня рассмотрим нашу пятую итерацию по созданию продукта для машинного обучения. Чтобы подойти к этой теме, кратко напомним о предыдущих продуктах и их состоянии на текущий момент. Рассмотрим только полностью интегрированные решения, которые позволяют пройти путь от расчета модели до использования в реальных кейсах в одном полноценном продукте.

🔗 Начинаем работу с Azure Machine Learning service
Сегодня рассмотрим нашу пятую итерацию по созданию продукта для машинного обучения. Чтобы подойти к этой теме, кратко напомним о предыдущих продуктах и их...

​I Built My Own Self-Driving Car. Part #1

In this part of article I am going to build a very basic car detection classifier using Python and OpenCV. There is a variety of different object detection and classification techniques and I am going to pay particular attention to the use of Haar Cascades. However, the Haar Cascade classification itself would be covered very briefly in this particular article.

#programming #IT #artificialintellegence #developer #HaarCascade #Python #OpenCV #MadDevs

https://blog.maddevs.io/i-built-my-own-self-driving-car-part-1-4478551ee205

🔗 I Built My Own Self-Driving Car. Part #1 – Mad Devs

​Другой GitHub: репозитории по Data Science, визуализации данных и глубокому обучению

Наш телеграмм канал - https://t.me/ai_machinelearning_big_data

🔗 Другой GitHub: репозитории по Data Science, визуализации данных и глубокому обучению