This article briefly describes what LLM embeddings are and shows how to use them as engineered features for Scikit-learn models.
Revisiting k-Means: 3 Approaches to Make It Work Better
This tutorial will explore three of the most effective techniques to make k-means work better in the wild, specifically using k-means++ for smarter centroid initialization, leveraging the silhouette score to find the optimal number of clusters, and applying the kernel trick to handle non-spherical data.
Discussing Decision Trees: What Makes a Good Split?
This article takes a closer look at the inner workings of decision trees, focusing on how branches are created through deliberate, data-driven splitting (spoiler: it certainly doesn’t happen at random).
7 Pandas Tricks That Cut Your Data Prep Time in Half
In this article, you will discover seven practical Pandas tips that can speed up your data prep process and help you focus more on analysis and less on cleanup.
Word Embeddings for Tabular Data Feature Engineering
This tutorial will guide you through a practical application of using pre-trained word embeddings to generate new features for a tabular dataset.
Decision Trees Aren’t Just for Tabular Data
Decision trees can accommodate data in diverse formats, beyond just fully structured, tabular data. This article examines this facet of decision trees from a balanced theoretical and practical approach.
10 NumPy One-Liners to Simplify Feature Engineering
In this article, we’ll explore 10 powerful NumPy one-liners that can simplify your feature engineering workflow.
Your First OpenAI API Project in Python Step-By-Step
Check out this step-by-step guide to setting up a Python project that enables interaction with state-of-the-art OpenAI models like GPT-4.
Securing FastAPI Endpoints for MLOps: An Authentication Guide
In this tutorial, we will build a straightforward machine learning application using FastAPI. Then, we will guide you on how to set up authentication for the same application, ensuring that only users with the correct token can access the model to generate predictions.
Skip Connections in Transformer Models
Transformer models consist of stacked transformer layers, each containing an attention sublayer and a feed-forward sublayer. These sublayers are not directly connected; instead, skip connections combine the input with the processed output in each sublayer. In this post, you will explore skip connections in transformer models. Specifically: Why skip connections are essential for training deep […]