Mary Boryslawska – Medium

Mary Boryslawska

Mary Boryslawska

Unraveling the Differences: Graph Neural Networks (GNNs) and Graph Convolutional Networks (GCNs)

For evaluating and processing graph-structured data, two important approaches include graph convolutional networks (GCNs) and graph neural…

Jun 25, 2023

Unraveling the Differences: Graph Neural Networks (GNNs) and Graph Convolutional Networks (GCNs)

Jun 25, 2023

Mary Boryslawska

Transforming Energy Forecasting: Harnessing the Power of Transformers

The adoption of AI techniques into different sectors has been going through the biggest boom yet. That includes the challenges faced by the…

May 26, 2023

Transforming Energy Forecasting: Harnessing the Power of Transformers

May 26, 2023

Mary Boryslawska

Shapley Values in Machine Learning

What does Game Theory have to do with Machine learning? What does Machine learning have to do with cocktails? As we’re about to find out…

Apr 7, 2022

Shapley Values in Machine Learning

Apr 7, 2022

Mary Boryslawska

ML Strategy — what can make it successful? — part 1

Why strategizing?

Oct 29, 2021

ML Strategy — what can make it successful? — part 1

Oct 29, 2021

Mary Boryslawska

t-SNE: data visualisation using t-distributed Stochastic Neighbour Embeddings

Overview

Aug 25, 2021

t-SNE: data visualisation using t-distributed Stochastic Neighbour Embeddings

Aug 25, 2021

Mary Boryslawska

Deep neural networks: How does regularisation prevent overfitting?

What really is overfitting? Overfitting is a very dangerous game many of us have played. It means that you get a low training dataset error…

Aug 5, 2021

Deep neural networks: How does regularisation prevent overfitting?

Aug 5, 2021

Mary Boryslawska

Are neural networks actually based on the brain?

The fascination with neural networks began with humans’ interest in the brain structure.

Jul 15, 2021

Jul 15, 2021

Mary Boryslawska

Mary Boryslawska

ML Engineer and Mathematician with extensive experience in transdisciplinary research, specialising in Machine Learning applications for renewable energy.

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