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

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

aparrish

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_]*$'

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

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

rxwei

See the official Differentiable Programming Manifesto instead.

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: