Prof. Chris Manning, Director of StanfordAILab & founder of Stanfordnlp, shared inspiring thoughts on research trends and challenges in #computervision and #NLP at #CVPR2019. View full interview:

http://bit.ly/2KR21hO

✴️ @AI_Python_EN

شرکت Amazon یه سری کورس رایگان گذاشته که قبلا فقط برای کارمندانش بوده. این آموزشها شامل 9 بخشه که سه بخش اولش شامل:
🔸NLP
🔸Computer Vision
🔸Tabular Data
میشه. اگه به ماشین لرنینگ و دیتاساینس علاقه دارید، فرصت رو از دست ندید.
Youtube channel

متریال آموزشی هم اینجاست👇
https://github.com/aws-samples/aws-machine-learning-university-accelerated-nlp

https://github.com/aws-samples/aws-machine-learning-university-accelerated-cv

https://github.com/aws-samples/aws-machine-learning-university-accelerated-tab

#machinelearning #datascience #computervision #nlp #artificialintelligence #course

@pythonicAi

شرکت Amazon یه سری کورس رایگان گذاشته که قبلا فقط برای کارمندانش بوده. این آموزشها شامل 9 بخشه که سه بخش اولش شامل:
🔸NLP
🔸Computer Vision
🔸Tabular Data
میشه. اگه به ماشین لرنینگ و دیتاساینس علاقه دارید، فرصت رو از دست ندید.
Youtube channel

متریال آموزشی هم اینجاست👇
https://github.com/aws-samples/aws-machine-learning-university-accelerated-nlp

https://github.com/aws-samples/aws-machine-learning-university-accelerated-cv

https://github.com/aws-samples/aws-machine-learning-university-accelerated-tab

#machinelearning #datascience #computervision #nlp #artificialintelligence #course

@pythonicAi

​​SwinIR: Image Restoration Using Swin Transformer

Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy, and compressed images). While state-of-the-art image restoration methods are based on convolutional neural networks, few attempts have been made with Transformers, which show impressive performance on high-level vision tasks.

The authors use a model SwinIR based on the Swin Transformers. Experimental results demonstrate that SwinIR outperforms state-of-the-art methods on different tasks (image super-resolution, image denoising, and JPEG compression artifact reduction) by up to 0.14~0.45dB, while the total number of parameters can be reduced by up to 67%.

Paper

Code

detailed overview

#deeplearning #transformer #computervision

@pythonicAi