An example of a Multi Layer Perception (MLP) Artificial Neural Network (ANN) for reading handwritten numbers (OCR).
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# Hello, and welcome to makefile basics. | |
# | |
# You will learn why `make` is so great, and why, despite its "weird" syntax, | |
# it is actually a highly expressive, efficient, and powerful way to build | |
# programs. | |
# | |
# Once you're done here, go to | |
# http://www.gnu.org/software/make/manual/make.html | |
# to learn SOOOO much more. |
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#include <stdio.h> | |
#include <stdlib.h> | |
#include <sys/time.h> | |
//#define ITERS 1000000 | |
void dgemv_(char *, int*, int *, double*, double*, int*, double*, int*, double*, double*, int*); | |
int main(int argc, char * argv[]) | |
{ | |
int m, n; |
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v0.7.0 ------------------------------------------- | |
node@[1m[33mv0.12.4 [39m[22m: [1m[31mFAIL[39m[22m [3mwrote output to /tmp/nan-dnt-v0.12.4.out[23m | |
node@[1m[33mv0.12.3 [39m[22m: [1m[31mFAIL[39m[22m [3mwrote output to /tmp/nan-dnt-v0.12.3.out[23m | |
node@[1m[33mv0.12.2 [39m[22m: [1m[31mFAIL[39m[22m [3mwrote output to /tmp/nan-dnt-v0.12.2.out[23m | |
node@[1m[33mv0.12.1 [39m[22m: [1m[31mFAIL[39m[22m [3mwrote output to /tmp/nan-dnt-v0.12.1.out[23m | |
node@[1m[33mv0.12.0 [39m[22m: [1m[31mFAIL[39m[22m [3mwrote output to /tmp/nan-dnt-v0.12.0.out[23m | |
node@[1m[33mv0.10.38[39m[22m: [1m[31mFAIL[39m[22m [3mwrote output to /tmp/nan-dnt-v0.10.38.out[23m | |
node@[1m[33mv0.10.37[39m[22m: [1m[31mFAIL[39m[22m [3mwrote output to /tmp/nan-dnt-v0.10.37.out[23m | |
node@[1m[33mv0.10.36[39m[22m: [1m[31mFAIL[39m[22m [3mwrote output to /tmp/nan-dnt-v0.10.36.out[23m | |
node@[1m[33mv0.10.35[39m[22m: [1m[32mPASS[39m[22m [3mwrote output to /tmp/nan-dnt-v0.10.35.out[2 |
- Distributed systems = Computers + relativity
- Different machines have different clocks, perception of time, and views of the state of the system
- Challenges: Communication failures, hardware failures, latency, bandwidth
- All computer systems today are distributed at some level (nature obeys relativity after all), the extent to which this nature is exposed depends on the loads put upon the system.
Here is a list of scopes to use in Sublime Text 2/3 snippets -
ActionScript: source.actionscript.2
AppleScript: source.applescript
ASP: source.asp
Batch FIle: source.dosbatch
C#: source.cs
C++: source.c++
Clojure: source.clojure
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#!/usr/bin/ruby | |
# For an OO language, this is distinctly procedural. Should probably fix that. | |
require 'json' | |
details = Hash.new({}) | |
capture_params = [ | |
{ :name => "title", :message => "Enter project name." }, | |
{ :name => "url", :message => "Enter the URL of the project repository." }, |
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""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """ | |
import numpy as np | |
import cPickle as pickle | |
import gym | |
# hyperparameters | |
H = 200 # number of hidden layer neurons | |
batch_size = 10 # every how many episodes to do a param update? | |
learning_rate = 1e-4 | |
gamma = 0.99 # discount factor for reward |