msampathkumar

 #
 # Copyright 201 Google Inc. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #      http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software

saranshdhingra

 #
 # Copyright 201 Google Inc. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #      http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software

msampathkumar

# Extracted using: $ unzip -p lib/pycharm.jar com/jetbrains/python/PyBundle.properties | grep -B1 INSP.NAME | grep '^#' | sed 's|Inspection||g' | sed -e 's|#\s\{,1\}|# noinspection |'

# noinspection PyPep8
# noinspection PyPep8Naming
# noinspection PyTypeChecker
# noinspection PyAbstractClass
# noinspection PyArgumentEqualDefault
# noinspection PyArgumentList
# noinspection PyAssignmentToLoopOrWithParameter
# noinspection PyAttributeOutsideInit

abritinthebay

// Colors reference
// You can use the following as so:
// console.log(colorCode, data);
// console.log(`${colorCode}some colorful text string${resetCode} rest of string in normal color`);
// 
// ... and so on.

export const reset = "\x1b[0m"
export const bright = "\x1b[1m"
export const dim = "\x1b[2m"

ibraaaa

import collections

import gym
import numpy as np
import tensorflow as tf

from gym import wrappers

ENV_ID = 'CartPole-v0'
SAVE_PATH = '/tmp/cartpole-experiment-3'

offchan42

ATTENTION PLEASE

This gist has been moved to https://github.com/offchan42/machine-learning-curriculum

Please see that repository instead because you can make pull requests there and later updates will be pushed there too.

===

Machine Learning Curriculum

fchollet

'''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

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

evenv

# A simple cheat sheet of Spark Dataframe syntax
# Current for Spark 1.6.1

# import statements
from pyspark.sql import SQLContext
from pyspark.sql.types import *
from pyspark.sql.functions import *

#creating dataframes
df = sqlContext.createDataFrame([(1, 4), (2, 5), (3, 6)], ["A", "B"]) # from manual data

vasanthk

System Design Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

Basic Steps

1. Clarify and agree on the scope of the system

• User cases (description of sequences of events that, taken together, lead to a system doing something useful)
  • Who is going to use it?
  • How are they going to use it?