Two Minute Papers explores the paper “ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness ” The source documents and code are available at the following links:
YouTuber carykh explores GANs that create customized faces of imaginary celebrities.
Using neural networks can you generate generate new, never before seen Garfield comics? CodeParade explains how it works.
Here’s a great video explaining Convolutional Neural Networks (CNNs), a type of neural network used in computer vision scenarios.
Two Minute Papers highlights some of the advances made in GANs over the last 18 months or so.
The paper “Large Scale GAN Training for High Fidelity Natural Image Synthesis” is available here:
- Paper: https://arxiv.org/abs/1809.11096
- Try it here: https://colab.research.google.com/git…
This Kaggle explains the inner workings of transfer learning, a fascinating application of neural networks.
Edwin shared a tweet that encapsulates all the common AI terms
Whatever you need to know about #AI#ML #DL #ComputerVision@MikeQuindazzi @evankirstel @psb_dc @diioannid @SpirosMargaris @helene_wpli @Paula_Piccard @mclynd @andi_staub @ipfconline @LouisSerge @jerome_joffre @ahier #edmuke @Ym78200 @3itcom pic.twitter.com/GMvcm6Mt22 @jblefevre60
— Edwin (@edmuke) November 12, 2018
Another great video from DeepLearning.TV.
At the recent Azure AI Fest, I had mentioned the work being done to train self-driving car AIs using video games like Grand Theft Auto V.
And, yes, Virginia, a kid on his own PC at home can compete with a multi-million dollar company at the same task. For reference, see Facebook, Microsoft, and Apple. True story.
How did OpenAI’s team of 5 neural networks manage to beat some of the world’s best DOTA 2 players?
Watch this video by Siraj Raval to learn how the team did it and what it means for AI research.
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