No Call to Idea videos yet. You could help us improve this page by suggesting one.
Based on our record, Papers with Code seems to be more popular. It has been mentiond 23 times since March 2021. We are tracking product recommendations and mentions on Reddit, HackerNews and some other platforms. They can help you identify which product is more popular and what people think of it.
Really depends on where you are coming from. If you are already working in Deep Learning, and super comfortable in some other framework, then- -> Head over to the PyTorch website, and go through the introductory tutorials. -> Go to Papers with Code , and start reading and trying out implementing relatively easier research papers. If you are a beginner, then, you can go through resources that reach Deep Learning... - Source: Hacker News / 6 days ago
As far as more practical stuff goes, if all you want is more time spent actually coding and using a library (vs building up a deeper theoretical understanding) then just get rolling with implementing some papers to start. There's all kinds of really cool directions you can go... Pick a problem and implement a few different important historical landmarks. On papers with code it's pretty easy to find a problem... - Source: Reddit / 6 days ago
There is the website paperswithcode. This website compile paper and their implementation on github. So you look at a loooot of Pytorch repository. - Source: Reddit / 6 days ago
You'll usually see RNNs in language modeling, time series forecasting, etc., while CNNs come up a lot in image recognition and other computer vision tasks. A good resource is Papers with Code (https://paperswithcode.com). They have some pages on methods, so you can see where RNNs and CNNs have been used in papers over the past 20 or so years:. - Source: Reddit / 30 days ago
Do you know https://paperswithcode.com/ ? You can find the code for the great majority of ML Papers there. - Source: Reddit / about 2 months ago
Curator - The visual notes app, now on iPhone!
ML5.js - Friendly machine learning for the web
MakeML - Train Neural Networks without a line of code
Machine Learning Weekly - A hand-picked newsletter in machine learning & deep learning
appealing - Mobile UI animations found in the wild
mlblocks - A no-code Machine Learning solution. Made by teenagers.