stympy

{
	# Caddy is running behind an application load balancer hosted at AWS, so this configures Caddy to trust the headers set by it
	servers {
		trusted_proxies static private_ranges
	}

	# Avoid DoS attacks by confirming with a backend app that a requested domain should have an on-demand certificate generated
	on_demand_tls {
		ask http://web.internal:5000/confirm_domain
		interval 1m

prologic

Learn Go in ~5mins

This is inspired by A half-hour to learn Rust and Zig in 30 minutes.

Basics

Your first Go program as a classical "Hello World" is pretty simple:

First we create a workspace for our project:

rxwei

See the official Differentiable Programming Manifesto instead.

johnhw

### 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

ddevault

WAYLAND_PROTOCOLS=/usr/share/wayland-protocols

# wayland-scanner is a tool which generates C headers and rigging for Wayland
# protocols, which are specified in XML. wlroots requires you to rig these up
# to your build system yourself and provide them in the include path.
xdg-shell-protocol.h:
	wayland-scanner server-header \
		$(WAYLAND_PROTOCOLS)/stable/xdg-shell/xdg-shell.xml $@

xdg-shell-protocol.c: xdg-shell-protocol.h

adrianhall

---
Description: AWS AppSync Notes API

Parameters:
  APIName:
    Type: String
    Description: Name of the API - used to generate unique names for resources
    MinLength: 3
    MaxLength: 20
    AllowedPattern: '^[a-zA-Z][a-zA-Z0-9_]*$'

aparrish

karpathy

"""
A bare bones examples of optimizing a black-box function (f) using
Natural Evolution Strategies (NES), where the parameter distribution is a 
gaussian of fixed standard deviation.
"""

import numpy as np
np.random.seed(0)

# the function we want to optimize

karpathy

""" 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

yrevar

{0: 'tench, Tinca tinca',
 1: 'goldfish, Carassius auratus',
 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',
 3: 'tiger shark, Galeocerdo cuvieri',
 4: 'hammerhead, hammerhead shark',
 5: 'electric ray, crampfish, numbfish, torpedo',
 6: 'stingray',
 7: 'cock',
 8: 'hen',
 9: 'ostrich, Struthio camelus',