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Sherman–Morrison formula - Wikipedia
computes the inverse of the sum of an invertible matrix A {\displaystyle A} A and the outer product, u v T {\displaystyle uv^{T}} uv^{T}, of vectors u {\displaystyle u} u and v {\displaystyle v} v.
matrix  maths 
2 days ago by dill
The matrix calculus you need for deep learning
Excellent (re-)primer on Calculus as it applies to "deep learning", a.k.a. neural networks.
neuroscience  neural_network  Markov  machine_learning  deep_learning  AI  artificial  intelligence  math  calculus  matrix  vector  scalar  partial_derivative  slope  gradient 
12 days ago by Tonti
Efficient matrix multiplication
GitHub is where people build software. More than 28 million people use GitHub to discover, fork, and contribute to over 85 million projects.
performance  matrix  programming  math  linear-algebra 
15 days ago by rryan
The Matrix Calculus You Need for Deep Learning | Hacker News
luk32 5 hours ago [-]

If someone likes more lecture style explanation I can recommend 3blue1brown's material on YouTube. He explained in a pretty good an accessible way imho.
I didn't learn artificial neural network stuff from there. I knew those concepts but I didn't know the matrix formalism applied to it. So this was really nice to understand why GPUs are good for this. Math-wise it was really nice watch.

tw1010 4 hours ago [-]
ml  math  ai  matrix 
18 days ago by aquaman73
The matrix calculus you need for deep learning
This paper is an attempt to explain all the matrix calculus you need in order to understand the training of deep neural networks. We assume no math knowledge beyond what you learned in calculus 1, and provide links to help you refresh the necessary math where needed. Note that you do not need to understand this material before you start learning to train and use deep learning in practice; rather, this material is for those who are already familiar with the basics of neural networks, and wish to deepen their understanding of the underlying math. Don't worry if you get stuck at some point along the way---just go back and reread the previous section, and try writing down and working through some examples. And if you're still stuck, we're happy to answer your questions in the Theory category at Note: There is a reference section at the end of the paper summarizing all the key matrix calculus rules and terminology discussed here.
machinelearning  deeplearning  matrix  calculus  ai  math 
18 days ago by euler

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