This tutorial provides a clear introduction to logarithms, their properties, and their common applications in machine learning.
Author Archive | Matthew Mayo
Kernel Methods in Machine Learning with Python
In this tutorial, we will explore the fundamentals of kernel methods, focusing on explaining the kernel trick, using SVMs for classification with kernel functions, dimensionality reduction using kernel PCA, and practical examples in Python.
YOU SEE AN LLM HERE: Integrating Language Models Into Your Text Adventure Games
Introduction Text-based adventure games have a timeless appeal. They allow players to imagine entire worlds, from shadowy dungeons and towering castles to futuristic spacecraft and mystic realms, all through the power of language. Today, integrating large language models (LLMs), like ChatGPT, into these games takes this concept to new heights by providing dynamically generated descriptions, […]
5 Common Mistakes to Avoid When Training LLMs
Introduction Training large language models (LLMs) is an involved process that requires planning, computational resources, and domain expertise. Data scientists, machine learning practitioners, and AI engineers alike can fall into common training or fine-tuning patterns that could compromise a model’s performance or scalability. This article aims to identify five common mistakes to avoid when training […]
5 Tips for Avoiding Common Rookie Mistakes in Machine Learning Projects
It’s easy enough to make poor decisions in your machine learning projects that derail your efforts and jeopardize your outcomes, especially as a beginner. While you will undoubtedly improve in your practice over time, here are five tips for avoiding common rookie mistakes and cementing your project’s success to keep in mind while you are […]
5 DIY Python Functions for Data Cleaning
Data cleaning: whether you love it or hate it, you likely spend a lot of time doing it. It’s what we signed up for. There’s no understanding, analyzing, or modeling data without first cleaning it. Making sure we have reusable tools handy for data cleaning is essential. To that end, here are 5 DIY functions […]
5 Effective Ways to Handle Imbalanced Data in Machine Learning
Introduction Here’s a something that new machine learning practitioners figure out almost immediately: not all datasets are created equal. It may now seem obvious to you, but had you considered this before undertaking machine learning projects on a real world dataset? As an example of a single class vastly outnumbering the rest, take for instance […]
Tips for Choosing the Right Machine Learning Model for Your Data
Introduction Choosing the right machine learning model for your data is of major importance in any data science project. The model you select will have a significant impact on the insights you derive from your data, and ultimately determine the usefulness of a project. In this article, we aim to provide practical tips to help […]
Tips for Deploying Machine Learning Models Efficiently
Introduction The process of deploying machine learning models is an important part of deploying AI technologies and systems to the real world. Unfortunately, the road to model deployment can be a tough one. The process of deployment is often characterized by challenges associated with taking a trained model — the culmination of a lengthy data-preparation […]
Tips for Handling Imbalanced Data in Machine Learning
Introduction Imperfect data is the norm rather than the exception in machine learning. Comparably common is the binary class imbalance when the classes in a trained data remains majority/minority class, or is moderately skewed. Imbalanced data can undermine a machine learning model by producing model selection biases. Therefore in the interest of model performance and […]