Tensor wrangling with TensorFlow

Tensor wrangling with TensorFlow

Softmax function, or normalized exponential function can be used to represent a categorical distribution, i.e. for probability computation in probability theory.

The YUSAG Football Model

The YUSAG Football Model

Let's start by reading in the NCAA FCS football data from 2013-2016:

The YUSAG Football Model

The YUSAG Football Model

Let's start by reading in the NCAA FCS football data from 2013-2016:

ANALISIS DE COMPONENTES PRINCIPALES

ANALISIS DE COMPONENTES PRINCIPALES

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

Fast_Multi Booster

Fast_Multi Booster

Data from Kaggle Otto competition

The YUSAG Football Model

The YUSAG Football Model

Let's start by reading in the NCAA FBS football data from 2013-2016:

California kindergarten vaccination analysis

California kindergarten vaccination analysis

This notebook documents that analysis conducted for the August 13, 2017, Los Angeles Times story "Despite California's strict new law, hundreds of schools still don't have enough vaccinated kids."

This is similar to the 'CNN' notebook, but will use pure tensorflow.

This is similar to the 'CNN' notebook, but will use pure tensorflow.

Here's the structure we want to mimic:

Bringing Functional Programming into an Imperative World.

Bringing Functional Programming into an Imperative World.

`https://github.com/gignosko/DjangoCon_2017`

Usage - python-pandas-datareader-NHSDigital

Usage - python-pandas-datareader-NHSDigital

63 rows × 6 columns

Randomized SVD demos

Randomized SVD demos

To use language in machine learning (for instance, how Skype translator translates between languages, or how Gmail Smart Reply automatically suggests possible responses for your emails), we need to represent words as vectors.

Here I will use PyTorch to show how backpropagation can be used to modify dynamics on the MullerPotential using a ...

Here I will use PyTorch to show how backpropagation can be used to modify dynamics on the MullerPotential using a ...

In stat. mech literature, this technique is called Metadynamics. Basically, the force being applied to a particle is dependent on the accumalated bias as the particular value of feature/collective variable. This comes down to a bunch of chain rules, all ...

Modeling and Simulation in Python

Modeling and Simulation in Python

Copyright 2017 Allen Downey

3-sphere: charts, quaternions and Hopf fibration

3-sphere: charts, quaternions and Hopf fibration

Click here to download the worksheet file (ipynb format). To run it, you must start SageMath with the Jupyter notebook, via the command sage -n jupyter

Linear models with CNN features

Linear models with CNN features

But these approaches have some downsides:

Prediction of chemical exchange observables from Markov models

Prediction of chemical exchange observables from Markov models

With this Jupyter notebook I provide some minimal examples on how to compute some common NMR observables sensitive to chemical exchange. The aim is to help people who want to apply the methodology we recently presented in our manuscript "Mechanistic ...

Clustering Search Volumes Based On Seasonal Search Spikes

Clustering Search Volumes Based On Seasonal Search Spikes

Through similarity measures (dynamic time warping), machine learing (K Means clustering) and some good luck we can do this.

Expectation Maximisation with Python : Coin Toss

Expectation Maximisation with Python : Coin Toss

See: http://www.nature.com/nbt/journal/v26/n8/full/nbt1406.html

PyGNS3 Examples

PyGNS3 Examples

Github repository / PyPi

A Game to Benchmark Quantum Computers

A Game to Benchmark Quantum Computers

This is a game to be played on quantum computers. It is designed so that it can run on the prototype devices of 2017, but it'll only really reach its potential on the fault-tolerant quantum computers of the future. From ...

Using Queue Runners to Feed Images Directly from Disk

Using Queue Runners to Feed Images Directly from Disk

Other code examples and content are available on GitHub . The PDF and ebook versions of the book are available through Leanpub .

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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Think like a Pythonista

Think like a Pythonista

Now, for the main event...

Kernel Mixture Network in PyTorch

Kernel Mixture Network in PyTorch

I refer the reader to the fantastic blog post of Jan van der Vegt where he explains in detail different ways of unconditional and conditional density estimation, and the application of Kernel Mixture Networks for this purpose. This notebook is ...

What Does It Take to Be An Expert At Python

What Does It Take to Be An Expert At Python

If you want to become an expert in Python, you should definitely watch this PyData talk from James Powell.

Anlálisis Exploratorio: Creadores de Contenido, Amplifcadores, Bursters, Eventuales

Anlálisis Exploratorio: Creadores de Contenido, Amplifcadores, Bursters, Eventuales

Previamente, basado en el análisis sobre el excel provisto por Yamila Abbas ,se determinaron algunos posibles marcadores para detectar lo que en Jugada Preparada se entiende que son usuarios o automatizados, o con comportamiento anómalo. Si bien aparentemente en la ...

Using Input Pipelines to Read Data from TFRecords Files

Using Input Pipelines to Read Data from TFRecords Files

Other code examples and content are available on GitHub . The PDF and ebook versions of the book are available through Leanpub .

What else can we tune in the neural network?

What else can we tune in the neural network?

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.

Delivered by Fastly , Rendered by Rackspace

Datas collected in https://www.nsf.gov/statistics/nsf13327/content.cfm?pub_id=4266&id=2

Datas collected in https://www.nsf.gov/statistics/nsf13327/content.cfm?pub_id=4266&id=2

5 rows × 33 columns