# My small corner of the internet

# Building a Convolution Neural Network For Fashion Data

In this post we will build a Convolution Neural Network (CNN) in order to classify images from the fashion MNIST dataset. CNNs are commonly used with image data to efficiently exploit spatial relationships between nearby pixels.

# Bootstrapped Regression Coefficients

Here we explore the theoretical coefficient distributions from a linear regression model. When fitting a regression model we can get estimates for the standard deviation of the coefficients. We use bootstrapping to get an empiracle distribution of the regression coefficients to compare against those distributions.

# Exploring parquet datasets

Parquet files are a columinar data format we can use to store dataframes. They can be stored in partitions, which can allow us to load only a subset of the data. This is useful is we are filtering the data, as we can do that without loading it all into memory.

# Fitting a Distribution with Pyro: Part 2 - Beta

This follows on from the previous post on fitting a gaussian distribution with pyro:
Fitting a Distribution with Pyro

# Fitting a Distribution with Pyro

In this simple example we will fit a Gaussian distribution to random data from a gaussian with some known mean and standard deviation.
We want to estimate a distribution that best fits the data using variational inference with Pyro.