Media is too big
VIEW IN TELEGRAM
Neural MMO(Massively Multiplayer Online Game) by OpenAI
How do you Simulate evolution on Earth?
First ever neural MMO environment, that has created agents(players) that would scale up to real-world complexity.
Massive Multiplayer Online Role-Playing Games are currently the best proxy for the real world humans. Making an environment multiplayer would include diverse skills, global economy, etc. parameters
GitHub Link: https://lnkd.in/gEXEvXd
Blog: https://lnkd.in/gQXuSWJ
Paper: https://lnkd.in/gkbNJJR
#pytorch #deeplearning #artificialintelligence #opensource #neuralnetworks #gaming #agents
✴️ @AI_Python_EN
How do you Simulate evolution on Earth?
First ever neural MMO environment, that has created agents(players) that would scale up to real-world complexity.
Massive Multiplayer Online Role-Playing Games are currently the best proxy for the real world humans. Making an environment multiplayer would include diverse skills, global economy, etc. parameters
GitHub Link: https://lnkd.in/gEXEvXd
Blog: https://lnkd.in/gQXuSWJ
Paper: https://lnkd.in/gkbNJJR
#pytorch #deeplearning #artificialintelligence #opensource #neuralnetworks #gaming #agents
✴️ @AI_Python_EN
Francesco Cardinale
I'm happy to announce that we just open-sourced a major update for our image super-resolution project: using an adversarial network and convolutional feature maps for the loss, we got some interesting results in terms realism and noise cancellation.
Pre-trained weights and GANs training code are available on GitHub!
If you want to read up about the process, check out the blog post.
Also, we released a pip package, 'ISR' (admittedly not the most creative name:D), with a nice documentation and colab notebooks to play around and experiment yourself on FREE GPU(#mindblown). Thanks to Dat Tran for the big help.
💻Blog: https://lnkd.in/dUnvXQZ
📝Documentation: https://lnkd.in/dAuu2Dk
🔤Github: https://lnkd.in/dmtV2ht
📕Colab (prediction): https://lnkd.in/dThVb_p
📘Colab (training): https://lnkd.in/diPTgWj
https://lnkd.in/dVBaKv4
#opensource #deeplearning #gans #machinelearning #keras
✴️ @AI_Python_EN
I'm happy to announce that we just open-sourced a major update for our image super-resolution project: using an adversarial network and convolutional feature maps for the loss, we got some interesting results in terms realism and noise cancellation.
Pre-trained weights and GANs training code are available on GitHub!
If you want to read up about the process, check out the blog post.
Also, we released a pip package, 'ISR' (admittedly not the most creative name:D), with a nice documentation and colab notebooks to play around and experiment yourself on FREE GPU(#mindblown). Thanks to Dat Tran for the big help.
💻Blog: https://lnkd.in/dUnvXQZ
📝Documentation: https://lnkd.in/dAuu2Dk
🔤Github: https://lnkd.in/dmtV2ht
📕Colab (prediction): https://lnkd.in/dThVb_p
📘Colab (training): https://lnkd.in/diPTgWj
https://lnkd.in/dVBaKv4
#opensource #deeplearning #gans #machinelearning #keras
✴️ @AI_Python_EN
The basics of NLP and real time sentiment analysis with open source tools
#NLP #sentimentanalysis #opensource
The basics of NLP and real time sentiment analysis with open source tools
✴️ @AI_Python_EN
#NLP #sentimentanalysis #opensource
The basics of NLP and real time sentiment analysis with open source tools
✴️ @AI_Python_EN
image_2019-06-15_17-40-10.png
657.8 KB
Erin LeDell: Happy to share our #ICML2019 #AutoML Workshop paper, "An Open Source AutoML Benchmark". We present a new #opensource AutoML benchmarking system and include results on: H2O AutoML, auto-sklearn, TPOT, Auto-WEKA
📰 Paper: https://www.automl.org/wp-content/uploads/2019/06/automlws2019_Paper45.pdf
👩💻 Code: https://github.com/openml/automlbenchmark/
✴️ @AI_Python_EN
📰 Paper: https://www.automl.org/wp-content/uploads/2019/06/automlws2019_Paper45.pdf
👩💻 Code: https://github.com/openml/automlbenchmark/
✴️ @AI_Python_EN