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Amazing visualizations of something that I dont understand at all. How a bunch of matrices encode information? All tutorials that I have seen are usually like t
by artemonster 3y ago
Amazing visualizations of something that I dont understand at all. How a bunch of matrices encode information? All tutorials that I have seen are usually like this: step 1: this is a neuron, step 2: lets do some random stuff in python and see magic.
Where do I look for fundamental explanations?
- lamename 3y agoHave you seen the neural net series by Statquest? https://youtu.be/HGwBXDKFk9I?si=GxYKy1s996e6Q8-G https://youtu.be/HGwBXDKFk9I?si=GxYKy1s996e6Q8-G It builds a simple CNN and ends with a simple example of how multiple ReLU activation functions can approximate arbitrary curves.
- artemonster 3y agoYeah, but how learning function approximators answer text questions?
- lamename 3y agoHave you see this one? https://jalammar.github.io/illustrated-transformer/ https://jalammar.github.io/illustrated-transformer/
- deleted 3y ago[deleted]
- abrichr 3y ago3Blue1Brown is often recommended: https://www.youtube.com/watch?v=aircAruvnKk https://www.youtube.com/watch?v=aircAruvnKk
- machiaweliczny 3y agoI suggest you take a look at Micrograd inplementation and watch video tutorial about it from Karpathy. Also „Python Deep Learning” book is quite good. In short you need to understand: vector, linear combination, cross product, partial derivative, chain rule and finding global minimum. If you have basics of linear algebra it’s easy to grok this video. https://youtu.be/VMj-3S1tku0?si=6r9XRXofN6SOPkpl https://youtu.be/VMj-3S1tku0?si=6r9XRXofN6SOPkpl
- hellojebus 3y agoI second the Micrograd implementation vid, had to pause several times to look up some concepts.. but so far the best resource I've found in regards to gradients work in neural networks
- taliesinb 3y agoYou might gain some basic intuition from this (partially complete) Numpy tutorial I wrote for the Deep Learning Indaba: https://arrayalgebra.info https://arrayalgebra.info
- likhuva 3y agoIn my opinion, the fundamental explanations you seek lie in Probability Theory, not matrix theory. When it comes to ML, matrices are just implementation details. I highly suggest this set of notes: https://chrispiech.github.io/probabilityForComputerScientists/en/index.html https://chrispiech.github.io/probabilityForComputerScientist...
- vjeux 3y agoIf you have points in a 2d space (a sheet of paper) and you want to separate them into two, you can draw a line between the two. The equation of a line is y = ax + b. This is the equation of a neuron is you squint. So if you chain a bunch of neurons, you are basically drawing a bunch of lines to test whether points belong or not. With enough lines you can approximate any shape, like a circle. What neural network do is given enough examples, it finds the lines that are needed to separate the points to give the appropriate label.
- smaddox 3y agoIf you're interested in developing an intuitive understanding for how information (items and relations) can be represented in vectors and matricies, I highly recommend this paper titled "Hyperdimensional computing: An introduction to computing in distributed representation with high-dimensional random vectors": http://ww.robertdick.org/iesr/papers/kanerva09jan.pdf http://ww.robertdick.org/iesr/papers/kanerva09jan.pdf