S.No | Slack Handler | NAME(Author) | Last Updated | NBS LINK | ODS MSG LINK |
---|---|---|---|---|---|
1 | velavok | Alexander Kovalev | 9th Dec | https://nbviewer.jupyter.org/github/Yorko/mlcourse.ai/blob/master/jupyter_english/tutorials/plotly_tutorial_for_interactive_plots_sankovalev.ipynb | https://opendatascience.slack.com/archives/C91N8TL83/p1543267284291700 |
2 | altprof | Gleb Levitski | 9th Dec | http://nbviewer.jupyter.org/github/Yorko/mlcourse.ai/blob/master/jupyter_english/tutorials/basic_semi-supervised_learning_models_altprof.ipynb | https://opendatascience.slack.com/archives/C91N8TL83/p1543848630231700 |
3 | krotix | Aleksandr Korotkov | 9th Dec | https://nbviewer.jupyter.org/github/Yorko/mlcourse.ai/blob/master/jupyter_english/tutorials/yet_another_ensemble_learning_helper_aleksandr_korotkov.ipynb | https://opendatascience.slack.com/archives/C91N8TL83/p1544203317384100 |
4 | Archit Run |
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#from my repo | |
#https://github.com/AdityaSoni19031997/Machine-Learning/blob/master/AV/AV_Enigma_NLP_functional_api.ipynb | |
def preprocess_word(word): | |
# Remove punctuation | |
word = word.strip('\'"?!,.():;') | |
# Convert more than 2 letter repetitions to 2 letter | |
# funnnnny --> funny | |
word = re.sub(r'(.)\1+', r'\1\1', word) | |
# Remove - & ' |
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#!/usr/bin/env python | |
# encoding: utf-8 | |
import sys | |
def trace_calls_and_returns(frame, event, arg): | |
co = frame.f_code | |
func_name = co.co_name | |
if func_name == 'write': | |
# Ignore write() calls from print statements |
1.251.423.0 1.279.441.0 1.257.387.0 1.275.1078.0 1.249.88.0 1.275.1080.0 1.257.401.0 1.275.1744.0 1.275.1346.0 1.249.715.0
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# Installs PyTorch, PyTorch/XLA, and Torchvision | |
# Copy this cell into your own notebooks to use PyTorch on Cloud TPUs | |
# Warning: this may take a couple minutes to run | |
import collections | |
from datetime import datetime, timedelta | |
import os | |
import requests | |
import threading |
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import random | |
from itertools import chain, cycle, islice | |
import torch.utils.data as data | |
import matplotlib.pyplot as plt | |
from matplotlib.patches import Rectangle | |
import time | |
import torch | |
import numpy as np |
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import bz2 | |
import pickle | |
from django.conf import settings | |
from djang_redis import get_redis_connection | |
from tqdm import tqdm | |
from .constants import GOOGLE_WORD2VEC_MODEL_NAME | |
- https://www.w3.org/2001/sw/sweo/public/UseCases/FAO/
- presentation on event in 2010
- Yummly food processor
- FOODpedia, linked data for food products
- Semantic web and GS1 (presentation)
- Example of how to use a semantic web of food
- OpenFoodFacts food product ontology notes, you can download a full products RDF dump
- Linked open data for halal food products - nice usage example
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# File: KnuthMorrisPratt.py | |
# Author: Keith Schwarz (htiek@cs.stanford.edu) | |
# | |
# An implementation of the Knuth-Morris-Pratt (KMP) string-matching algorithm. | |
# This algorithm takes as input a pattern string P and target string T, then | |
# finds the first occurrence of the string T in the pattern P, doing so in time | |
# O(|P| + |T|). The logic behind the algorithm is not particularly complex, | |
# though getting it to run in linear time requires a few non-obvious tricks. | |
# | |
# To motivate KMP, consider the naive algorithm for trying to match a pattern |