Libardo Lopez Libardo1

La importancia del Software Libre en la Ciencia

Para el primer post en este blog deseo escribir sobre algo que realmente importa. Al menos para mí. Traté de llegar al punto medular de lo que está detrás de lo que me dedico: ¿Cuáles son las razones fundamentales por las que amo tanto al Software Libre?

Quick List of Resources for Topological Data Analysis with Emphasis on Machine Learning

This is just a quick list of resourses on TDA that I put together for @rickasaurus after he was asking for links to papers, books, etc on Twitter and is by no means an exhaustive list.

Survey Papers

Both Carlsson's and Ghrist's survey papers offer a very good introduction to the subject

Topology and Data by Gunnar Carlsson
Barcodes: The Persistent Topology of Data by Robert Ghrist

Other Papers and Web Resources

Extracting insights from the shape of complex data using topology A good introductory paper in Nature on the Mapper algorithm.

Horizons in Probabilistic Programming and Bayesian Analysis

Representations:

Hierarchical models
Hidden Markov models
Graphical models
Non-parametric Bayes (distributions over functions)

Inference Approaches:

	"""
	Code for training RBMs with contrastive divergence. Tries to be as
	quick and memory-efficient as possible while utilizing only pure Python
	and NumPy.
	"""

	# Copyright (c) 2009, David Warde-Farley
	# All rights reserved.
	#
	# Redistribution and use in source and binary forms, with or without

	# Network building
	net = tflearn.input_data([None, 100])
	net = tflearn.embedding(net, input_dim=10000, output_dim=128)
	net = tflearn.lstm(net, 128, dropout=0.8)
	net = tflearn.fully_connected(net, 2, activation='softmax')
	net = tflearn.regression(net, optimizer='adam', learning_rate=0.001,
	loss='categorical_crossentropy')

	# Training
	model = tflearn.DNN(net)

	from keras.layers import Recurrent
	import keras.backend as K
	from keras import activations
	from keras import initializers
	from keras import regularizers
	from keras import constraints
	from keras.engine import Layer
	from keras.engine import InputSpec

	import pandas as pd
	from random import random

	flow = (list(range(1,10,1)) + list(range(10,1,-1)))*100
	pdata = pd.DataFrame({"a":flow, "b":flow})
	pdata.b = pdata.b.shift(9)
	data = pdata.iloc[10:] * random() # some noise

	import numpy as np