# My small corner of the internet

# Time series: NeuralProphet speed test

Neural prophet is a time series forecasting library very similar to the Facebook prophet.
Neural prophet runs using pytorch, rather than fbprophet and itâ€™s use of pystan.
It has some potential advantages by using stochastic gradient descent.

# Do Neural Networks overfit?

This brief post is exploring overfitting neural networks. It comes from reading the paper:
Towards Understanding Generalization of Deep Learning: Perspective of Loss Landscapes

# Installed Energy Capacity in Europe

Here we will find some data to see what the state of renewable energy production is within Europe.
The data is collected from:
https://transparency.entsoe.eu/generation/r2/installedGenerationCapacityAggregation/show

# Linear regression to non linear probabilistic neural network

In this post I will attempt to go over the steps between a simple linear regression
towards a non-linear probabilistic model built with a neural network.

# Fitting a normal distribution with tensorflow probability

Here we look at fitting a normal distribution to some data using Tensorflow Probability.

# Batch Normalisation for a Convolution Neural Network

We compare the performance of adding a batch normalisation layer to a convolution neural network (CNN). For this we use the results from a previous post on creating a CNN for fashion MNIST data:

# 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