rondy

FWIW: I (@rondy) am not the creator of the content shared here, which is an excerpt from Edmond Lau's book. I simply copied and pasted it from another location and saved it as a personal note, before it gained popularity on news.ycombinator.com. Unfortunately, I cannot recall the exact origin of the original source, nor was I able to find the author's name, so I am can't provide the appropriate credits.

---

Effective Engineer - Notes

• By Edmond Lau
• Highly Recommended 👍
• http://www.theeffectiveengineer.com/

What's an Effective Engineer?

mblondel

# (C) Mathieu Blondel, November 2013
# License: BSD 3 clause

import numpy as np


def ranking_precision_score(y_true, y_score, k=10):
    """Precision at rank k

    Parameters

ed-alertedh

import tensorflow as tf


def validate_dataset(filenames, reader_opts=None):
    """
    Attempt to iterate over every record in the supplied iterable of TFRecord filenames
    :param filenames: iterable of filenames to read
    :param reader_opts: (optional) tf.python_io.TFRecordOptions to use when constructing the record iterator
    """
    i = 0

jgillman

# Assumes the database container is named 'db'

DOCKER_DB_NAME="$(docker-compose ps -q db)"
DB_HOSTNAME=db
DB_USER=postgres
LOCAL_DUMP_PATH="path/to/local.dump"

docker-compose up -d db
docker exec -i "${DOCKER_DB_NAME}" pg_restore -C --clean --no-acl --no-owner -U "${DB_USER}" -d "${DB_HOSTNAME}" < "${LOCAL_DUMP_PATH}"
docker-compose stop db

omoindrot

"""
Example TensorFlow script for finetuning a VGG model on your own data.
Uses tf.contrib.data module which is in release v1.2
Based on PyTorch example from Justin Johnson
(https://gist.github.com/jcjohnson/6e41e8512c17eae5da50aebef3378a4c)

Required packages: tensorflow (v1.2)
Download the weights trained on ImageNet for VGG:
```
wget http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz

epicserve

#!/usr/bin/env bash

# This script prints out all of your Redis keys and their size in a human readable format
# Copyright 2013 Brent O'Connor
# License: http://www.apache.org/licenses/LICENSE-2.0

human_size() {
        awk -v sum="$1" ' BEGIN {hum[1024^3]="Gb"; hum[1024^2]="Mb"; hum[1024]="Kb"; for (x=1024^3; x>=1024; x/=1024) { if (sum>=x) { printf "%.2f %s\n",sum/x,hum[x]; break; } } if (sum<1024) print "1kb"; } '
}

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

codeinthehole

from celery import chain

from django.core.management.base import BaseCommand

from . import tasks


class Command(BaseCommand):

    def handle(self, *args, **kwargs):

wrburgess

Grab new backup of database

Command: heroku pgbackups:capture --remote production

Response: >>> HEROKU_POSTGRESQL_COLOR_URL (DATABASE_URL) ----backup---> a712

Get url of backup download

Command: heroku pgbackups:url [db_key] --remote production

lioutasb

import tensorflow as tf

def mrr_metric(labels, predictions, weights=None,
              metrics_collections=None,
              updates_collections=None,
              name=None):
    
    with tf.name_scope(name, 'mrr_metric', [predictions, labels, weights]) as scope: