Chris-hughes10

jbschiratti

"""Transfer learning example on fake data."""

from collections import OrderedDict

import torch
import torch.nn.functional as F
from torch import optim
from torch.utils.data import DataLoader
from torchvision.models import resnet50

bradtraversy

MongoDB Cheat Sheet

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stared

morgangiraud

# Remove anything linked to nvidia
sudo apt-get remove --purge nvidia*
sudo apt-get autoremove

# Search for your driver
apt search nvidia

# Select one driver (the last one is a decent choice)
sudo apt install nvidia-370

ivanleoncz

#/usr/bin/python3
""" Demonstration of logging feature for a Flask App. """

from logging.handlers import RotatingFileHandler
from flask import Flask, request, jsonify
from time import strftime

__author__ = "@ivanleoncz"

import logging

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

jvmops

#####################################################
# Author: sdwsk
# Date: 2015/12/19
#####################################################

# 0.1 Directory structure.

/dev/                 # devices
/etc/                 # system wide configuration files
/etc/rsyslog.conf     # log config

LeCoupa

#!/bin/bash
#####################################################
# Name: Bash CheatSheet for Mac OSX
# 
# A little overlook of the Bash basics
#
# Usage:
#
# Author: J. Le Coupanec
# Date: 2014/11/04

why-not

"""making a dataframe"""
df = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'))

"""quick way to create an interesting data frame to try things out""" 
df = pd.DataFrame(np.random.randn(5, 4), columns=['a', 'b', 'c', 'd'])

"""convert a dictionary into a DataFrame"""
"""make the keys into columns"""
df = pd.DataFrame(dic, index=[0])