An illustrated and intuitive guide on how Neural Networks learn Welcome to Part 2 of the Deep Learning Illustrated series. In the previous article (definitely read that first!), we covered how a neural network works and how a trained neural network makes predictions. In this article, we’ll delve into the training process and explore how…

How should we choose between label, one-hot, and target encoding? 15 min read · 16 hours ago Why Do We Need Encoding? In the realm of machine learning, most algorithms demand inputs in numeric form, especially in many popular Python frameworks. For instance, in scikit-learn, linear regression, and neural networks require numerical…

Implementing a simple neural network framework from scratch Trees — the core of computation. Source: Adrian Infernus on Unsplash.Despite doing some work and research in the AI ecosystem for some time, I didn’t truly stop to think about backpropagation and gradient updates within neural networks until recently. This article seeks to rectify that and will…

Go from nothing to a complete dataframe with Python Photo by Joshua Sortino on Unsplash.After submitting a recent article to Towards Data Science’s editorial team, I received a message back with a simple inquiry: are the datasets licensed for commercial use? It was a great question — the datasets in my draft came from Seaborn,…

Practical lessons from upgrading Bed-Reader, a bioinformatics library Rust and Python reading DNA data directly from the cloud — Source: https://openai.com/dall-e-2/. All other figures from the author.Would you like your Rust program to seamlessly access data from files in the cloud? When I refer to “files in the cloud,” I mean data housed on web…

This blog post shows how to easily run an LLM locally and how to set up a ChatGPT-like GUI in 4 easy steps. Photo by Liudmila Shuvalova on UnsplashThanks to the global open source community, it is now easier than ever to run performant large language models (LLM) on consumer laptops or CPU-based servers and…

How Neural Networks are strong tools for solving differential equations without the use of training data Photo by Linus Mimietz on UnsplashDifferential equations are one of the protagonists in physical sciences, with vast applications in engineering, biology, economy, and even social sciences. Roughly speaking, they tell us how a quantity varies in time (or some…

A step-by-step illustration of how to use SOLID to solve a refactoring challenge Photo by Lucas Davies on UnsplashIntroduction Code refactor challenges are well-known by software engineers, but less so by data scientists, though data scientists can also highly benefit from practising such challenges. By practising these, especially when applying the SOLID principles, you learn…

I discovered the Himalayan Database a few weeks ago and decided to create a few “whimsical” visualizations based on this dataset. In two previous articles I created a simple elevation plot for Everest expeditions and a plot showing the relative number of deaths for 5 Himalayan peaks. This time I wanted to explore expedition accident…

Data comes in different shapes and forms. One of those shapes and forms is known as categorical data. This poses a problem because most Machine Learning algorithms use only numerical data as input. However, categorical data is usually not a challenge to deal with, thanks to simple, well-defined functions that transform them into numerical values.…