Best paper on ICCV2019
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 https://arxiv.org/abs/1905.01164
arXiv.org
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...
New cool post from Distill.Pub on computing receptive fields of CNN https://distill.pub/2019/computing-receptive-fields/
Distill
Computing Receptive Fields of Convolutional Neural Networks
Detailed derivations and open-source code to analyze the receptive fields of convnets.
Guys, today we launched our Object Detection course on Product Hunt. And that's mean that as never before we need your support and upvoting power
Please go by link: https://www.producthunt.com/posts/object-detection-with-pytorch
Upvote our project and give a comment.
Many thanks)
Please go by link: https://www.producthunt.com/posts/object-detection-with-pytorch
Upvote our project and give a comment.
Many thanks)
Unsupervised Cross-lingual Representation Learning at Scale https://twitter.com/alex_conneau/status/1192490719031656448?s=19
Twitter
Alexis Conneau
Our new paper: Unsupervised Cross-lingual Representation Learning at Scale https://t.co/N5nTKhUBnE We release XLM-R, a Transformer MLM trained in 100 langs on 2.5 TB of text data. Double digit gains on XLU benchmarks + strong per-language performance (~XLNet…
Hello, friends 🎉
After 2 months of sleepless nights and battles with gradients, we're happy to share with you our new online course about Machine Learning - “Object Detection with PyTorch”
http://learnml.today
You may ask why we did it if there are so many similar courses? - And that’s a good question 💛
💎 The reason zero - we like ML and we like to teach people, cause that's one of the greatest things that u can give back to the community - knowledge that was gained by hard work and a huge amount of time spent. So the next generation can go farther from where we stopped.
💎 The first reason - creating courses on some small area/problem/task to transfer knowledge to other specialists as fast as possible and without bullshit.
We are not professors from the university, we are practical guys who are paying their bills by doing researches and solutions in Machine Learning and AI areas. And as professionals, we often have a need to get new knowledge with deep understanding of problematic of tasks asap. But all we can find on the market is online courses about simple stuff that good for newcomers but not for us. And that’s a big problem because getting knowledge from papers is time-consuming.
💎 The second reason - a huge gap between studying materials and real tasks. I think many of you had this feeling in the past when the teacher describe you something like 2+2 = 4, and then in real life, you get task to calculate the trajectory of a spaceship to Mars.
So we want to build courses from developers for developers, with giving real practical knowledge without gaps, so students can be ready for real-life after the course ends.
💎 The course will lead you from the basics to the latest state-of-the-art solution and will consist of 7 lessons:
⭐ PyTorch Basics
⭐ Single Object Localisation
⭐ Single Shot Networks / Yolo
⭐ Single Shot Networks / SSD
⭐ Region Proposal Networks / Fast R-CNN
⭐ Region Proposal Networks / Mask R-CNN
⭐ Bonus Material
💎 Requirements: Python, Base Math, ML Basics (CNN networks, Dense Networks)
💎 Time: 4+ weeks (2 lessons/week but we will look by student progress)
💎 Interaction with students: GitHub Issues
💎 Environment(free): Google Colab, Ram: 12GB, Disk: 350GB, GPU: Nvidia T4 16GB
💎 Start date: 18 November 2019
Also we have small demo tutorial for you: http://bit.ly/traffic-counting-with-opencv
You can subscribe on course on our page http://learnml.today
After 2 months of sleepless nights and battles with gradients, we're happy to share with you our new online course about Machine Learning - “Object Detection with PyTorch”
http://learnml.today
You may ask why we did it if there are so many similar courses? - And that’s a good question 💛
💎 The reason zero - we like ML and we like to teach people, cause that's one of the greatest things that u can give back to the community - knowledge that was gained by hard work and a huge amount of time spent. So the next generation can go farther from where we stopped.
💎 The first reason - creating courses on some small area/problem/task to transfer knowledge to other specialists as fast as possible and without bullshit.
We are not professors from the university, we are practical guys who are paying their bills by doing researches and solutions in Machine Learning and AI areas. And as professionals, we often have a need to get new knowledge with deep understanding of problematic of tasks asap. But all we can find on the market is online courses about simple stuff that good for newcomers but not for us. And that’s a big problem because getting knowledge from papers is time-consuming.
💎 The second reason - a huge gap between studying materials and real tasks. I think many of you had this feeling in the past when the teacher describe you something like 2+2 = 4, and then in real life, you get task to calculate the trajectory of a spaceship to Mars.
So we want to build courses from developers for developers, with giving real practical knowledge without gaps, so students can be ready for real-life after the course ends.
💎 The course will lead you from the basics to the latest state-of-the-art solution and will consist of 7 lessons:
⭐ PyTorch Basics
⭐ Single Object Localisation
⭐ Single Shot Networks / Yolo
⭐ Single Shot Networks / SSD
⭐ Region Proposal Networks / Fast R-CNN
⭐ Region Proposal Networks / Mask R-CNN
⭐ Bonus Material
💎 Requirements: Python, Base Math, ML Basics (CNN networks, Dense Networks)
💎 Time: 4+ weeks (2 lessons/week but we will look by student progress)
💎 Interaction with students: GitHub Issues
💎 Environment(free): Google Colab, Ram: 12GB, Disk: 350GB, GPU: Nvidia T4 16GB
💎 Start date: 18 November 2019
Also we have small demo tutorial for you: http://bit.ly/traffic-counting-with-opencv
You can subscribe on course on our page http://learnml.today
Machine Learning World pinned «Hello, friends 🎉 After 2 months of sleepless nights and battles with gradients, we're happy to share with you our new online course about Machine Learning - “Object Detection with PyTorch” http://learnml.today You may ask why we did it if there are so many…»
Create 3D scene from 1-2 images
https://arxiv.org/abs/1911.04554
https://arxiv.org/abs/1911.04554
How make smaller and faster network by knowledge got from big one?
https://blog.floydhub.com/knowledge-distillation/
https://blog.floydhub.com/knowledge-distillation/
IMG_9851.mp4
13.6 MB
DeepFake that we deserved
South Korea has created an entire city for testing self-driving cars with 35 kinds of road test facilities. Here is the video from BuzzFeed news