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Title | Deep Learning @ |
Description | Recent Toggle navigation Deep Learning @ Home Home Links Archive About RSS --> Blog about doing deep learning at home. Recent Posts Pong from pixels in Pytorc |
Keywords | N/A |
WebSite | deeplearningathome.com |
Host IP | 52.217.170.21 |
Location | United States |
Site | Rank |
US$263,620
Last updated: 2023-05-14 01:26:12
deeplearningathome.com has Semrush global rank of 40,149,911. deeplearningathome.com has an estimated worth of US$ 263,620, based on its estimated Ads revenue. deeplearningathome.com receives approximately 30,418 unique visitors each day. Its web server is located in United States, with IP address 52.217.170.21. According to SiteAdvisor, deeplearningathome.com is safe to visit. |
Purchase/Sale Value | US$263,620 |
Daily Ads Revenue | US$244 |
Monthly Ads Revenue | US$7,301 |
Yearly Ads Revenue | US$87,603 |
Daily Unique Visitors | 2,028 |
Note: All traffic and earnings values are estimates. |
Host | Type | TTL | Data |
deeplearningathome.com. | A | 299 | IP: 52.217.170.21 |
deeplearningathome.com. | NS | 86400 | NS Record: ns-1608.awsdns-09.co.uk. |
deeplearningathome.com. | NS | 86400 | NS Record: ns-35.awsdns-04.com. |
deeplearningathome.com. | NS | 86400 | NS Record: ns-924.awsdns-51.net. |
deeplearningathome.com. | NS | 86400 | NS Record: ns-1236.awsdns-26.org. |
Toggle navigation Deep Learning @ Home Home Links Archive About RSS --> Blog about doing deep learning at home. Recent Posts Pong from pixels in Pytorch Deep Learning PC - Upgrade Simple LSTM based language model in PyTorch vs Tensorflow Deep Learning State Of The Art Compressing MNIST digits using AutoEncoder Links Deep learning PC build Deep learning book Tensorflow CUDA CNTK Sep 20, 2018 - Pong from pixels in Pytorch Comments --> Intro This post simply converts code from Andrej Karpathy’s excellent post into Pytorch. (Here left is game’s built-in AI and right is the one played by a simple 2 layer FC network). There main differenes with Andrej’s code are: It uses Pytorch autograd and some other handy functions It is organized around specifying loss function weighted by advantage instead of manipulating gradients direcly It does run some things on GPU The code is in Jupyter notebooks and is located here. It needs very long time to converge (many hours). Currently, it’s use of |
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