skyzyx

macOS is a Unix, and not built on Linux.

I think most of us realize that macOS isn't a Linux OS, but what that also means is that instead of shipping with the GNU flavor of command line tools, it ships with the FreeBSD flavor. As such, writing shell scripts which can work across both platforms can sometimes be challenging.

Homebrew

Homebrew can be used to install the GNU versions of tools onto your Mac, but they are all prefixed with "g" by default.

All commands have been installed with the prefix "g". If you need to use these commands with their normal names, you can add a "gnubin" directory to your PATH from your bashrc.

simongrest

joelgrus

"""
a response to https://gist.github.com/TomAugspurger/f96d41ae7a40d41fe9053d0adbceb4f1
"""

from collections import deque
import datetime
import itertools
import random
import string
from typing import NamedTuple, Tuple, Iterable, List

WaylonWalker

yufengg

lunaluxie

import tensorflow as tf
tf.reset_default_graph()

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

### model ###

# input
with tf.name_scope('input') as scope:

tomokishii

1. mnist_cnn.py - baseline code (till TF 1.2)
2. mnist_estimator.py - using TF 1.3 tf.estimator API

jnothman

from sklearn.feature_extraction import DictVectorizer

class PandasVectorizer(DictVectorizer):
    def fit(self, x, y=None):
        return super(PandasVectorizer, self).fit(x.to_dict('records'))
    
    def fit_transform(self, x, y=None):
        return super(PandasVectorizer, self).fit_transform(x.to_dict('records'))
    
    def transform(self, x):

dgrtwo

library(tidyverse)

# Data is downloaded from here:
# https://www.kaggle.com/c/digit-recognizer
kaggle_data <- read_csv("~/Downloads/train.csv")

pixels_gathered <- kaggle_data %>%
  mutate(instance = row_number()) %>%
  gather(pixel, value, -label, -instance) %>%
  extract(pixel, "pixel", "(\\d+)", convert = TRUE)

mahermalaeb

import zipfile
from surprise import Reader, Dataset, SVD, evaluate

# Unzip ml-100k.zip
zipfile = zipfile.ZipFile('ml-100k.zip', 'r')
zipfile.extractall()
zipfile.close()

# Read data into an array of strings
with open('./ml-100k/u.data') as f: