npearce

UPDATE (March 2020, thanks @ic): I don't know the exact AMI version but yum install docker now works on the latest Amazon Linux 2. The instructions below may still be relevant depending on the vintage AMI you are using.

Amazon changed the install in Linux 2. One no-longer using 'yum' See: https://aws.amazon.com/amazon-linux-2/release-notes/

Docker CE Install

sudo amazon-linux-extras install docker
sudo service docker start

danijar

# Full example for my blog post at:
# https://danijar.com/building-variational-auto-encoders-in-tensorflow/

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

tfd = tf.contrib.distributions

viecode09

STEP 1: First Install HomeBrew, download it from http://brew.sh

$ ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

STEP 2: Install Hadoop

$ brew search hadoop
$ brew install hadoop

diegopacheco

# Remove java 7
sudo yum remove -y java

# Install basic packages
sudo yum install -y git

# Download and install java 8
wget --no-cookies --no-check-certificate --header "Cookie: gpw_e24=http%3A%2F%2Fwww.oracle.com%2F; oraclelicense=accept-securebackup-cookie" "http://download.oracle.com/otn-pub/java/jdk/8u131-b11/d54c1d3a095b4ff2b6607d096fa80163/jdk-8u131-linux-x64.tar.gz"
tar -xzvf jdk-8u131-linux-x64.tar.gz

danijar

# Working example for my blog post at:
# https://danijar.github.io/structuring-your-tensorflow-models
import functools
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data


def doublewrap(function):
    """
    A decorator decorator, allowing to use the decorator to be used without

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?

vinhkhuc

"""
Vanilla Char-RNN using TensorFlow by Vinh Khuc (@knvinh).
Adapted from Karpathy's min-char-rnn.py
https://gist.github.com/karpathy/d4dee566867f8291f086
Requires tensorflow>=1.0
BSD License
"""
import random
import numpy as np
import tensorflow as tf

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

ngenator

def bellman_ford(graph, source):
    # Step 1: Prepare the distance and predecessor for each node
    distance, predecessor = dict(), dict()
    for node in graph:
        distance[node], predecessor[node] = float('inf'), None
    distance[source] = 0

    # Step 2: Relax the edges
    for _ in range(len(graph) - 1):
        for node in graph:

econchick

class Graph:
  def __init__(self):
    self.nodes = set()
    self.edges = defaultdict(list)
    self.distances = {}

  def add_node(self, value):
    self.nodes.add(value)

  def add_edge(self, from_node, to_node, distance):