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Introduction

Introduction

To be more precise, we trained FCN-32s , FCN-16s and FCN-8s models that were described in the paper "Fully Convolutional Networks for Semantic Segmentation" by Long et al. on PASCAL VOC Image Segmentation dataset and got similar accuracies compared to ...

Column Transpose Decipher

Column Transpose Decipher

This note book is an attempt to solve the text decryption problem posted at http://www.isi.edu/natural-language/people/transpose.html

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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TF-Slim Walkthrough

TF-Slim Walkthrough

Installation and setup Creating your first neural network with TF-Slim Reading Data with TF-Slim Training a convolutional neural network (CNN) Using pre-trained models

Comparing Python Clustering Algorithms

Comparing Python Clustering Algorithms

To start, lets' lay down some ground rules of what we need a good EDA clustering algorithm to do, then we can set about seeing how the algorithms available stack up.

5 Useful Data Wrangling Techniques Using Python Pandas and data.world

5 Useful Data Wrangling Techniques Using Python Pandas and data.world

What this tutorial is :

Get data from yahoo, after the ichart interface is gone.

Get data from yahoo, after the ichart interface is gone.

On 18 May 2017 the ichart data api of yahoo finance went down, without any notice. And it does not seem like it is coming back. This has left many (including me) without a descent free end-of-day data source.

Using functional programming in Python like a boss: Generators, Iterators and Decorators

Using functional programming in Python like a boss: Generators, Iterators and Decorators

When you write:

プログラマーのための確率プログラミングとベイズ推定

プログラマーのための確率プログラミングとベイズ推定

ベイズ推定(Bayesian method)は,確率推論のためのもっとも適切なアプローチであるにもかかわらず,書籍を読むとページ数も数式も多いので,あまり積極的に読もうとする読者は少ないのが現状である.典型的なベイズ推定の教科書では,最初の3章を使って確率の理論を説明し,それからベイズ推論とは何かを説明する.残念ながら多くのベイズモデルは解析的に解くことが困難であるため,読者が目にするのは簡単で人工的な例題ばかりになってしまう.そのため,ベイス推論と聞いても「だから何?」と思ってしまうのである.実際,著者の私がそう思っていたのだから.

Unit I: Optimization

Unit I: Optimization

Combinatorial optimization refers to searching for the best combination of design decisions for stated design objectives. It offers a rigorous yet actionable formalism for HCI.

Introduction to PyCall

Introduction to PyCall

PyCall の実態は「 Ruby から libpython.so を使うための拡張ライブラリ 」です。 PyCall は libpython.so の機能を利用して、Ruby から Python のオブジェクトを触れるようにするブリッジ機能を提供します。 PyCall を使うと、例えば以下のように Python 側の sin 関数を Ruby 側に持ってきて呼び出すことが可能です。

Author: Carlos Góes

Author: Carlos Góes

Retrieves data from 2013-2015

NLP tools for Twitter

NLP tools for Twitter

Consists of functions I've developed to clean and parse these tweets into a format suited for analysis and for use in the context of deep learning / machine learning applications.

Application of Neural Networks in Finance

Application of Neural Networks in Finance

-Dynamically load daily price history from Yahoo / Google Finance -Apply some feature engineering -Train XGBoost and NN Models to predict if the direction of the next day's price move -Re-Train based on the most important Feature Columns -Predict using ...

Standard Deviation vs. Interquartile Range

Standard Deviation vs. Interquartile Range

Standard deviation is the square root of variance, which is the sum of squares of the deviation from the mean. In small sample sizes, such as a NFL season, a single outlier game can distort the mean. There is a ...

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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Hello

Hello

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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.

Delivered by Fastly , Rendered by Rackspace

Core Techniques used in our ETL

Core Techniques used in our ETL

They are a highlighted technique in this workshop because they provide:

daru + rbplotly + statsample のデモ

daru + rbplotly + statsample のデモ

↑ なんとなく違いがありそう

Memo

Memo

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

In this notebook, we will show how to load pre-trained models and draw things with sketch-rnn

In this notebook, we will show how to load pre-trained models and draw things with sketch-rnn

define the path of the model you want to load, and also the path of the dataset

Introduction to PyCall

Introduction to PyCall

PyCall の実態は「 Ruby から libpython.so を使うための拡張ライブラリ 」です。 PyCall は libpython.so の機能を利用して、Ruby から Python のオブジェクトを触れるようにするブリッジ機能を提供します。 PyCall を使うと、例えば以下のように Python 側の sin 関数を Ruby 側に持ってきて呼び出すことが可能です。

XGBoost GPU Benchmarks

XGBoost GPU Benchmarks

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.

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

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Porque Charles Xavier debe cambiar a Cerebro por Python

Porque Charles Xavier debe cambiar a Cerebro por Python

Marvel es madre de archiconocidos personajes o equipos como:

7. Multi-variable Calculus

7. Multi-variable Calculus

From now on, let $\vec{\mathbf{x}} = (x^1, \cdots, x^n) \in \mathbb{R}$ and $\overrightarrow{\mathbf{x}_0} = (x_0^1, \cdots, x^n_0) \in \mathbb{R}^n$. Also let $f_i (\vec{x}) = \frac{\partial f}{\partial x^i}$ be the $i$-th partial derivative.

7. Multi-variable Calculus

7. Multi-variable Calculus

Suppose that $(x_0, y_0)$ is in the domain of $z = f (x, y)$ 1. the partial derivative with respect to $x$ at $(x_0, y_0)$ is the limit

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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