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The Language for future-julia
1.
1
2.
2
3.
3
4.
python ★ 29.2k golang ★ 68.7k nodejs ★
67.6k rust ★ 38,548
5.
5
6.
6 Rapid development Production Readable
& modifiable Performance
7.
7
8.
a = [1,
2, 3, 4, 5] function square(x) return x^2 end for x in a println(square(x)) end 8
9.
https://julialang.org/benchmarks/ 9
10.
10
11.
https://juliacomputing.com/case-studies/laketide.html
12.
https://juliacomputing.com/case-studies/mit-robotics.html
13.
https://juliacomputing.com/case-studies/ny-fed.html 13 https://github.com/FRBNY-DSGE/DSGE.jl
14.
https://juliacomputing.com/case-studies/rna.html
15.
https://juliacomputing.com/case-studies/circuitscape.html http://maps.tnc.org/migrations-in-motion/
16.
https://juliacomputing.com/case-studies/intel-astro.html 20
17.
https://www.nature.com/articles/d41586-019-02310-3
18.
https://github.com/JuliaRegistries/General/blob/master/Registry.toml 22
19.
23
20.
24 https://docs.juliatw.org/latest/
21.
25
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26
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24.
28
25.
29
26.
30 VimEmacsVscodeSublime IntelliJ
27.
31
28.
32 μ = 0 σ
= 1 normal = Normal(μ, σ)
29.
33
30.
34
31.
for i =
1:100_000 do_something() end Threads.@threads for i = 1:100_000 do_something() end 35
32.
Julia mode: julia> using
Pkg julia> Pkg.update() julia> Pkg.add(“Foo”) julia> Pkg.rm(“Foo”) 36 Pkg mode: v(1.3) pkg> update V(1.3) pkg> add Foo v(1.3) pkg> rm Foo
33.
julia> @code_native add(1,
2) .text Filename: REPL[2] pushq %rbp movq %rsp, %rbp Source line: 2 leaq (%rcx,%rdx), %rax popq %rbp retq nopw (%rax,%rax) function add(a, b) return a+b end 37
34.
julia> @code_llvm add(1,
2.0) ; Function Attrs: uwtable define double @julia_add_71636(i64, double) #0 { top: %2 = sitofp i64 %0 to double %3 = fadd double %2, %1 ret double %3 } function add(a, b) return a+b end 38
35.
48
36.
49 https://juliastats.org/
37.
50
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51
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52
40.
53
41.
54 Bootstrap CategoricalArrays Clustering CSV DataFrames Distances Distributions GLM HypothesisTests KernelDensity Loess MultivariateStats StatsBase TimeSeries
42.
julia> using DataFrames julia>
df = DataFrame(A = 1:4, B = ["M", "F", "F", "M"]) 4× 2 DataFrame │ Row │ A │ B │ ├─────┼───┼───┤ │ 1 │ 1 │ M │ │ 2 │ 2 │ F │ │ 3 │ 3 │ F │ │ 4 │ 4 │ M │ 55
43.
julia> df[:A] 4-element Array{Int64,1}: 1 2 3 4 julia>
df[2, :A] 2 56
44.
julia> using CSV julia>
df = CSV.read("data.csv") julia> df = DataFrame(A = 1:10); julia> CSV.write("output.csv", df) 57
45.
julia> names =
DataFrame(ID = [1, 2], Name = ["John Doe", "Jane Doe"]) julia> jobs = DataFrame(ID = [1, 2], Job = ["Lawyer", "Doctor"]) julia> full = join(names, jobs, on = :ID) 2× 3 DataFrame │ Row │ ID │ Name │ Job │ ├─────┼────┼──────────┼────────┤ │ 1 │ 1 │ John Doe │ Lawyer │ │ 2 │ 2 │ Jane Doe │ Doctor │ 58
46.
julia> q1 =
@from i in df begin @where i.age > 40 @select {number_of_children=i.children, i.name} @collect DataFrame end 59
47.
63 julia> data =
DataFrame(X=[1,2,3], Y=[2,4,7]) 3x2 DataFrame |-------|---|---| | Row # | X | Y | | 1 | 1 | 2 | | 2 | 2 | 4 | | 3 | 3 | 7 |
48.
64 julia> OLS =
glm(@formula(Y ~ X), data, Normal(), IdentityLink()) DataFrameRegressionModel{GeneralizedLinearModel,Float64}: Coefficients: Estimate Std.Error z value Pr(>|z|) (Intercept) -0.666667 0.62361 -1.06904 0.2850 X 2.5 0.288675 8.66025 <1e-17
49.
65 julia> newX =
DataFrame(X=[2,3,4]); julia> predict(OLS, newX, :confint) 3× 3 Array{Float64,2}: 4.33333 1.33845 7.32821 6.83333 2.09801 11.5687 9.33333 1.40962 17.257 # The columns of the matrix are prediction, 95% lower and upper confidence bounds
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66
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67 # initialize the
attractor n = 1500 dt = 0.02 σ, ρ, β = 10., 28., 8/3 x, y, z = 1., 1., 1. # initialize a 3D plot with 1 empty series plt = plot3d(1, xlim=(-25,25), ylim=(-25,25), zlim=(0,50), xlab = "x", ylab = "y", zlab = "z", title = "Lorenz Attractor", marker = 1) # build an animated gif, saving every 10th frame @gif for i=1:n dx = σ*(y - x) ; x += dt * dx dy = x*(ρ - z) - y ; y += dt * dy dz = x*y - β*z ; z += dt * dz push!(plt, x, y, z) end every 10
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JuliaStats 68
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69
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70 https://julialang.org/blog/2017/12/ml&pl-zh_tw
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71Ref: https://venturebeat.com/2019/02/18/facebooks-chief-ai-scientist-deep-learning-may-need-a-new-programming-language/ Pic: https://xconomy.com/boston/2017/11/01/as-facebook-fights-fake-news-lecun-sees-bigger-role-for-a-i/
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2019.2.20 10 a.m.
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73 https://github.com/FluxML/Zygote.jl
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74 julia> using Zygote julia>
f(x) = 3x + 2 f (generic function with 1 method) julia> f(3.) 11.0 julia> f'(3.) 3.0
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75 julia> @code_llvm f'(3.) ;
Function Attrs: uwtable define double @"julia_#34_17010"(double) #0 { top: ret double 3.000000e+00 }
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78 Pic: https://blog.algorithmia.com/introduction-to-loss-functions/ Loss function Pic:
http://dsdeepdive.blogspot.com/2016/03/optimizations-of-gradient-descent.html Gradient
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79 for-loop, while-loop
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81 @model gdemo(x,
y) = begin # Assumptions σ ~ InverseGamma(2,3) μ ~ Normal(0,sqrt(σ)) # Observations x ~ Normal(μ, sqrt(σ)) y ~ Normal(μ, sqrt(σ)) end https://turing.ml/dev/
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82 https://turing.ml/dev/
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83 https://github.com/alan-turing-institute/MLJ.jl Integrate 109 models
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84 https://github.com/alan-turing-institute/MLJ.jl
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85 https://github.com/alan-turing-institute/MLJ.jl
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Next: Machine
Learning and Deep Learning on Quantum Computing 86 https://github.com/QuantumBFS/Yao.jl
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87 https://github.com/JuliaGPU/CuArrays.jl
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90 http://www.stochasticlifestyle.com/co mparison-differential-equation-solver- suites-matlab-r-julia-python-c-fortran/
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91 Objective types • Linear •
Convex Quadratic • Nonlinear (convex and nonconvex) Constraint types • Linear • Convex Quadratic • Second-order Conic • Semidefinite • Nonlinear (convex and nonconvex) Variable types • Continuous • Integer-valued • Semicontinuous • Semi-integer
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95 https://mobile.twitter.com/KenoFischer/status/1158517084642582529
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96 https://juliacon.org/2020/
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https://julialang.org/teaching/
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