Here’s a great video explaining Convolutional Neural Networks (CNNs), a type of neural network used in computer vision scenarios.
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.
All neural networks use activation functions, but the reasons behind using them are never clear!
In this video, the great Siraj Raval discusses what activation functions are, when they should be used, and what the difference between them is.
Arxiv Insights dives into Variational Autoencoders (VAEs), a class of neural networks that can learn to compress data completely unsupervised.
VAE’s are a very hot topic right now in unsupervised modelling of latent variables and provide a unique solution to the curse of dimensionality.
Siraj Raval explains how neuroscience and machine learning are related as well as how the two fields will help each other advance.