# Fine Tuning GPT-2 for Magic the Gathering Flavour Text Generation

A template for fine-tuning your own GPT-2 model.

GPT-3 has dominated the NLP news cycle recently with its borderline magical performance in text generation, but for everyone without \$1,000,000,000 of Azure compute credits there are still plenty of ways to experiment with language models on your own. Hugging Face is a free open source company focussing on NLP tooling and they provide one of the easiest ways of accessing pre-trained models and tokenizers for NLP experiments. In this article, I will share a method for fine tuning the 117M parameter GPT-2 model with a corpus of Magic the Gathering card…

# Machine Learning Building Blocks: Logistic Regression

## Preamble

In my previous article, I wrote about linear regression, starting with linear equations and analytical solutions to fitting your data, to gradient descent-optimized models and using PyTorch primitives to create a single layer neural network to solve a continuous linear regression problem. This time, I will be giving an explanation of how to make discrete classifications using logistic regression on a binary breast cancer data set.

## Introduction

In the ScikitLearn Breast Cancer data set, we have labels for benign or malignant tumours and a matrix of thirty features which describe each of 569 breast tumours to make the predictions on. I…

# Machine Learning Building Blocks: Linear Regression

## Linear regression from scratch using Pytorch and Autograd

Neural network frameworks, automl solutions and staple numerical libraries, like Scikit-learn and SciPy, have abstracted away much of the logic and math from the implementation of workhorse algorithms. Linear regressions fall into this category. Regressions are fundamental techniques that are often as performant as more complicated models, but we sometimes underestimate them as simply “drawing a line through the data”. This oversimplification leads us to forget that the most basic neural network is infact just a linear regression with no hidden layers, meaning that understanding linear regressions is key to understanding deep learning techniques. …

## Richard Bownes

BBC Data Scientist

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