debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

endolith

gene1wood

This gist contains lists of modules available in

• Python 3.10
• Python 3.9
• Python 3.8
• Python 3.7
• Python 3.6
• Python 2.7

in AWS Lambda.

PhilippLgh

var Bot = require('node-telegram-bot-api')
var watson = require('watson-developer-cloud');
var request = require('request');
var config = require('./config');

var speech_to_text = watson.speech_to_text({
  username: config.watson.username,
  password: config.watson.password,
  version: 'v1',
  url: 'https://stream.watsonplatform.net/speech-to-text/api'

AllanLRH

% ****************************************************************************
% *  This demonstrate how to apply blur to an RGB-image, and create a third  *
% *    image which is composed partly of the original unblurred image, and   *
% *                       partly of the blurred image.                       *
% ****************************************************************************


% Read example image built into MATLAB
I0 = double(imread('saturn.png'));

fperez

Polyglot Data Science with IPython & friends

Author: Fernando Pérez.

A demonstration of how to use Python, Julia, Fortran and R cooperatively to analyze data, in the same process.

This is supported by the IPython kernel and a few extensions that take advantage of IPython's magic system to provide low-level integration between Python and other languages.

See the companion notebook for data preparation and setup.

mkocabas

#!/usr/bin/env bash
mkdir coco
cd coco/

wget http://images.cocodataset.org/zips/train2017.zip
wget http://images.cocodataset.org/zips/val2017.zip

unzip train2017.zip
unzip val2017.zip

akashpalrecha

devymex

import sys, io, os.path as path, functools as ft      # basic packages
import urllib.request as url_req, numpy as np, cv2    # extension packages
import torch, torch.nn as nn, torchvision.ops as ops  # pytorch packages

data_path = '.' if len(sys.argv) < 2 else sys.argv[1]
image_file_url = 'https://github.com/pjreddie/darknet/raw/master/data/dog.jpg'
image_filename = path.join(data_path, 'dog.jpg')
weight_file_url = 'https://pjreddie.com/media/files/yolov3.weights'
weight_filename = path.join(data_path, 'yolov3.weights')

muellerzr