Sublime
An inspiration engine for ideas
If you’re bored with today’s neural net architectures, then my advice to you is to start training models to use an external memory, now that RL is finally working. https://t.co/qXm1PRECSp
99% of all life by biomass learns just by mutating DNA, no neurons. Artificial neural nets taught us a lot, but to truly understand intelligence we also need to recreate DNA.
I made a repo on artificial DNA. It solves tic-tac-toe reliably in 30min.
https://t.co/FaAovV7i3e... See more
Some blogs change your perception, and this one by Andrej Karpathy is one of those. https://t.co/j5FCONS39x
most foundational concept in deep learning that no one understands is probably the Neural Tangent Kernel (NTK)
this line of work studies neural networks of *infinite width*, which explain a lot about normal finite-width NNs
and there is exactly one Very Good blog post on them: https://t.co/kiuFITciFh
Artificial Neural Networks (ANN) simulate neural networks found in humans and animals. The human brain’s neural network has 100 billion neurons, interconnected by thousands or more synapses each. Each neuron may fire based on synaptic input. This multilayer neural network is capable of making a single decision based on thousands or more inputs.