Protected: Programming HOWTOs
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Using LAPACK in C, julia, lisp, and python
LAPACK and BLAS are two very highly optimized code libraries for linear algebra. There is almost no way that you can write linear algebra code that can compete with them in speed, accuracy or economy. Julia, R, python (and practically
Using libc and gsl in julia and lisp (and python too)
Don’t reinvent the wheel. Your computer system libraries probably contain routines to efficiently calculate almost any function that you might need to use. You can install a wrapper package for these libraries (such as libc, gsl, …) or simply use
Code management: patch and diff
Suppose that your collaboration has its code-base on a git server and you are a developer for it. You make a pull/clone of the active branch and examine the file CG.jl, which is for computing Clebsch-Gordon coefficients using formula 2.34
Code managment: Using git
A large collaboration (well, any collaboration) may maintain a code-base for the in-house software that the group has written for their research. Each collaborator needs to be able to pull down the code-base development branch, make contributions, and commit them
Auto-differentiation: make a julia package
We have talked about auto-differentiation before, but you can’t over-hype this programming technique. Everything is made easy if calculation of a function and its exact derivative can be done simultaneously at low cost. In every modern language there are auto-diff
Module/package creation in julia
We have previously talked about package creation in lisp and in R, and library creation in C. Packages or modules for julia are pretty easy to build, so let’s put together an example. In your shell, go into your development
Protected: AI/ML-6. ConvNets
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Protected: AI/ML-5. On Flux convolutional and pooling layers
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Homotopy continuation: solve transcendental equations
"= the julia library HomotopyContinuation is SLOW. This code is easy to entend and is actually pretty fast, and does not use 77 other libraries and packages, just four, and plotting is optional =" using DynamicPolynomials using LinearAlgebra using DataStructures
Protected: AI/ML-4.Image creation and manipulation in Julia
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Protected: AI/ML-3. Flux loss functions
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Protected: AI/ML-1. Neural nets in Julia
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