Introducción a los métodos de Monte-Carlo con Python

Introducción a los métodos de Monte-Carlo con Python

En el cierre de mi artículo anterior , comentaba sobre la sorprendente influencia que han tenido los números aleatorios , que junto con el poder de cálculo que nos proporcionan las computadoras modernas; nos han ayudado a resolver muchos de ...

Tipping point models

Tipping point models

There's a nice animation here .

Lecture 1: Boxes and Registers

Lecture 1: Boxes and Registers

The basic goal of this lecture is to understand why some code (in some languages and/or styles of coding) is slow while other code is fast, based on whether it can be compiled to efficiently use the CPU registers and ...

Proof of Concept — embedding a Bokeh server in a Notebook

Proof of Concept — embedding a Bokeh server in a Notebook

There are various application handlers that can be used to build up Bokeh documents. For example, there is a ScriptHandler that uses the code from a .py file to produce Bokeh documents. This is the handler that is used when ...

Generatate Zipf distributed data

Generatate Zipf distributed data

This website does not host notebooks, it only renders notebooks available on other websites.

Visualizing Convolution features using MNIST Dataset

Visualizing Convolution features using MNIST Dataset

This website does not host notebooks, it only renders notebooks available on other websites.

Let's start by importing all the libraries we will use.

Let's start by importing all the libraries we will use.

And creating a set of data to work with. It's a small set of data where $n=64$ because the implementation of LAP in this notebook isn't very fast.

Apollo 13 Transcript Exploratory Analysis

Apollo 13 Transcript Exploratory Analysis

The goal is to examine the transcript from the Apollo 13 mission to see if any interesting insights can be gleamed. The Apollo 13 was the famous mission to the moon that almost ended in disaster when an oxygen tank ...

Python Data Science Handbook

Python Data Science Handbook

This is the Jupyter notebook version of the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub .* The text is released under the CC-BY-NC-ND license , and code is released under the MIT license . ...

This website does not host notebooks, it only renders notebooks available on other websites.

This website does not host notebooks, it only renders notebooks available on other websites.

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The g-h Filter

The g-h Filter

Before we continue, some words about using Jupyter notebooks with this book. This book is interactive. If you want to run code examples, and especially if you want to see animated plots, you will need to run the code cells. ...

Using TensorFlow's TenosrBoard to project numpy embedding matrix to 3D/2D

Using TensorFlow's TenosrBoard to project numpy embedding matrix to 3D/2D

https://www.tensorflow.org/versions/master/how_tos/embedding_viz/

Getting started with Basemap: Post 1

Getting started with Basemap: Post 1

This is the first notebook which I have created to walk you through the project. In this notebook, I will get you accustomed to the data and concentrate on plotting the data on a world map. In the posts to ...

Deep Learning in Action

Deep Learning in Action

Goodfellow et al. 2016, Deep Learning

Views versus copies in NumPy

Views versus copies in NumPy

As its name is saying, it is simply another way of viewing the data of the array. Technically, that means that the data of both objects is shared . You can create views by selecting a slice of the original ...

This website does not host notebooks, it only renders notebooks available on other websites.

This website does not host notebooks, it only renders notebooks available on other websites.

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Estimating $\pi$ on the GPU

Estimating $\pi$ on the GPU

Monte Carlo simulation methods are computational algorithms which rely on repeated random sampling to estimate a results. Monte Carlo simulation can be used to calculate the value of $\pi$ as follows

Wordpress scraper

Wordpress scraper

Note: Worpress blogs have different configurations and versions. So the code has to be adapted by first inspecting the source code of the rendered html, then the soup.findAll has to be changed to fit either the permanent URL in Step ...

62 rows × 12 columns

62 rows × 12 columns

This website does not host notebooks, it only renders notebooks available on other websites.

Handling Missing Data

Handling Missing Data

The text is released under the CC-BY-NC-ND license , and code is released under the MIT license . If you find this content useful, please consider supporting the work by buying the book !

Emprical Cumulative Distribution Function on the GPU

Emprical Cumulative Distribution Function on the GPU

The empirical cumulative distribution function $\hat{F}_n(t)$ for the samples $x_1, \ldots, x_n$ is defined as

mbedオフライン開発環境の構築

mbedオフライン開発環境の構築

パッケージ管理ソフトHomebrewとXcodeをインストールします。

Deep Inverse Regression with Yelp reviews

Deep Inverse Regression with Yelp reviews

First, download to the same directory as this note the data from the Yelp recruiting contest on kaggle :

This website does not host notebooks, it only renders notebooks available on other websites.

This website does not host notebooks, it only renders notebooks available on other websites.

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Deep Learning in Action

Deep Learning in Action

Goodfellow et al. 2016, Deep Learning

This website does not host notebooks, it only renders notebooks available on other websites.

This website does not host notebooks, it only renders notebooks available on other websites.

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Tutorial

Tutorial

Import the Fabric module from text-fabric:

Introduction to Python OpenCV

Introduction to Python OpenCV

Let me know if you need help installing these dependencies; I would be happy to help.

Street network figure-ground diagrams, à la Allan Jacobs's Great Streets

Street network figure-ground diagrams, à la Allan Jacobs's Great Streets

This website does not host notebooks, it only renders notebooks available on other websites.

Cross entropy and maximum likelihood

Cross entropy and maximum likelihood

For classification problems, a commonly used loss (error) function in deep learning is cross entropy :