Alberto Labarga alabarga

## LMAsSprings.ipynb

      
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                QuentinAndre
                / LMAsSprings.ipynb
            
            
              Created
              January 21, 2019 15:11
            
          
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## image-classification-in-10-minutes-with-mnist-dataset.ipynb

      
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                Doctorwolfram
                / image-classification-in-10-minutes-with-mnist-dataset.ipynb
            
            
              Created
              January 1, 2019 16:57
            
              
                Image Classification in 10 Minutes with MNIST Dataset 
              
          
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## trolls_neural_networks.R
library(tidyverse)
library(kerasformula)
library(textfeatures)

tweets <- read_csv("https://raw.githubusercontent.com/fivethirtyeight/russian-troll-tweets/master/IRAhandle_tweets_1.csv",
                   n_max = 25000) %>%
  mutate(text = content)

features <- textfeatures(tweets, threads = 2)

## umap_sparse.py
### JHW 2018
import numpy as np
import umap


# This code from the excellent module at:
# https://stackoverflow.com/questions/4643647/fast-prime-factorization-module

import random

## marker_genes_visualization.ipynb

      
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                fidelram
                / marker_genes_visualization.ipynb
            
            
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              March 6, 2019 07:34
            
              
                example visualization of marker genes in clusters
              
          
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## flights.js
var fs = require('fs');
var request = require('request-promise');
var moment = require('moment')

// Globals
global.timestamp = moment().unix()
global.allPlaybacks = [];
global.geojson = {};
global.geojson['type'] = 'FeatureCollection';
global.geojson['features'] = [];

## Fastai_Colab_GPU_test.ipynb

      
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                roycoding
                / Fastai_Colab_GPU_test.ipynb
            
            
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              January 23, 2018 22:27
            
              
                fast.ai on Google Colaboratory with GPU test
              
          
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## 00_odsc_london.md

      
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                yhilpisch
                / 00_odsc_london.md
            
            
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              February 25, 2020 20:23
            
          
    ODSC Europe 2017 (London)

Algorithmic Trading
With Machine & Deep Learning
Workshop by Dr. Yves J. Hilpisch | The Python Quants GmbH
London, 12. October 2017

  
## catastroWebServices.md

      
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                fpsampayo
                / catastroWebServices.md
            
            
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              June 15, 2023 07:49
            
              
                Consultas de ejemplo a los servicios web de catastro
              
          
    Urls principales de los servicios:


Callejero: http://ovc.catastro.meh.es/ovcservweb/OVCSWLocalizacionRC/OVCCallejero.asmx
Callejero (códigos): http://ovc.catastro.meh.es/ovcservweb/OVCSWLocalizacionRC/OVCCallejeroCodigos.asmx
Coordenadas: http://ovc.catastro.meh.es//ovcservweb/OVCSWLocalizacionRC/OVCCoordenadas.asmx


## Python utilities.md

      
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                sloanlance
                / Python utilities.md
            
            
              Created
              June 28, 2017 15:30
            
              
                Python: A document of ready to use utilities included in Python's standard modules
              
          
    Python's Built-in Utilities

List of command line accessible tools built into the Python standard library.
Encoding and decoding

Base64 en/decoding:
python -m base64 -d [file]

python -m base64 -e [file]
	library(tidyverse)
	library(kerasformula)
	library(textfeatures)

	tweets <- read_csv("https://raw.githubusercontent.com/fivethirtyeight/russian-troll-tweets/master/IRAhandle_tweets_1.csv",
	n_max = 25000) %>%
	mutate(text = content)

	features <- textfeatures(tweets, threads = 2)
	### JHW 2018
	import numpy as np
	import umap



	# This code from the excellent module at:
	# https://stackoverflow.com/questions/4643647/fast-prime-factorization-module

	import random
	var fs = require('fs');
	var request = require('request-promise');
	var moment = require('moment')

	// Globals
	global.timestamp = moment().unix()
	global.allPlaybacks = [];
	global.geojson = {};
	global.geojson['type'] = 'FeatureCollection';
	global.geojson['features'] = [];