# Mathspp Blog

### A blog dedicated to mathematics and programming!

This blog has a really interesting assortment of articles on mathematics and programming. You can use the tags to your right to find topics that interest you, or you may want to have a look at

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##### Running Python inside ChatGPT

Running Python inside ChatGPT shows some amazing capabilities that ChatGPT has to understand Python code and evaluate it for you.

##### TIL #034 – multi-channel transposed convolution

Today I learned about multi-channel transposed convolutions.

##### TIL #033 – transposed convolution

Today I learned about the transposed convolution transformation in CNNs.

##### TIL #002 – fake chess AI

Today I learned about a fake chess-playing AI from the 1700s.

##### Neural networks fundamentals with Python – student-teacher

In this article of the NNFwP series we'll do the “student-teacher” experiment with two neural networks, where one network will learn directly from the other.

##### Neural networks fundamentals with Python – subtleties

In the fifth article of this short series we will be handling some subtleties that we overlooked in our experiment to classify handwritten digits from the MNIST dataset.

##### Neural networks fundamentals with Python – MNIST

In the fourth article of this short series we will apply our neural network framework to recognise handwritten digits.

##### Neural networks fundamentals with Python – backpropagation

The third article of this short series concerns itself with the implementation of the backpropagation algorithm, the usual choice of algorithm used to enable a neural network to learn.

##### Neural networks fundamentals with Python – network & loss

In the second article of this short series we will create a class for a generic neural network and we will also see how to assess the quality of the output of a network, essentially preparing ourselves to implement the backpropagation algorithm.

##### Neural networks fundamentals with Python – intro

This is the first article in a series to implement a neural network from scratch. We will set things up in terms of software to install, knowledge we need, and some code to serve as backbone for the remainder of the series.