| % (C) Mathieu Blondel, July 2010 | |
| \documentclass[a4paper,10pt]{article} | |
| \usepackage[english]{babel} | |
| \usepackage[T1]{fontenc} | |
| \usepackage[ansinew]{inputenc} | |
| \usepackage{lmodern} | |
| \usepackage{amsmath} |
This is not intended to be comprehensive or authoritative, just free online resources I've found valuable while learning more about Erlang.
| (defn setup [] | |
| (smooth) | |
| (no-fill)) | |
| (defn superformula-point [m n1 n2 n3 phi] | |
| (let [t1 (pow (abs (cos (/ (* m phi) 4.0))) n2) | |
| t2 (pow (abs (sin (/ (* m phi) 4.0))) n3) | |
| r (pow (+ t1 t2) (/ 1 n1))] | |
| (if (= 0 (abs r)) | |
| [0 0] |
| package main | |
| import ( | |
| "fmt" | |
| "os" | |
| "os/exec" | |
| "syscall" | |
| ) | |
| func main() { |
| n02119789 1 kit_fox | |
| n02100735 2 English_setter | |
| n02110185 3 Siberian_husky | |
| n02096294 4 Australian_terrier | |
| n02102040 5 English_springer | |
| n02066245 6 grey_whale | |
| n02509815 7 lesser_panda | |
| n02124075 8 Egyptian_cat | |
| n02417914 9 ibex | |
| n02123394 10 Persian_cat |
| '''This script goes along the blog post | |
| "Building powerful image classification models using very little data" | |
| from blog.keras.io. | |
| It uses data that can be downloaded at: | |
| https://www.kaggle.com/c/dogs-vs-cats/data | |
| In our setup, we: | |
| - created a data/ folder | |
| - created train/ and validation/ subfolders inside data/ | |
| - created cats/ and dogs/ subfolders inside train/ and validation/ | |
| - put the cat pictures index 0-999 in data/train/cats |
| ### JHW 2018 | |
| import numpy as np | |
| import umap | |
| # This code from the excellent module at: | |
| # https://stackoverflow.com/questions/4643647/fast-prime-factorization-module | |
| import random |
| """Implementation of NEAT. | |
| python neat.py --task {xor, lunar, cartpole} | |
| See the post at https://wellecks.wordpress.com/ for details. | |
| Parts of this implementation are based on Neat-Python. | |
| """ | |
| from itertools import count | |
| import numpy as np | |
| import math |
A good way to get a taste of Swift for Tensorflow language and tools is to set it up with Jupyter with the fastai Swift notebooks. I wanted a quick setup, which the Mac install experience currently not, so instead I installed the release binaries in a Ubuntu container via Docker. The setup process for this scenario is not well documented, so here it is for you / future me.
What we're about to do is install the S4TF 0.4 release and the fastai v3 Swift notebooks on Ubuntu 18.04. Generally we follow the swift-jupyter docker file, but install cpu-only release versions of the packages.
Below are some of the references I looked at:
https://github.com/tensorflow/swift/blob/master/docs/WhySwiftForTensorFlow.md https://github.com/tensorflow/swift/blob/master/docs/DifferentiableFunctions.md
