A Gentle Introduction to AutoML with Auto-WEKA
Auto-WEKA is a widely used AutoML software tool, as well as one of the more early available examples. In this tutorial, we will provide a gentle introduction to it.
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Introduction to Scikit-learn: A Beginner’s Guide
This beginner’s guide will introduce you to the basics of Scikit-learn, presenting its main features and explaining how it can help you tackle different machine learning tasks.
Introduction to Platform Engineering: Exploring Key Concepts, Principles, and Benefits
Platform engineering distinguishes itself through a systematic approach towards designing, building, and maintaining platforms, providing a solid foundation for multiple applications and services.
Thinking Fast & Slow: Tests for Large Language Models Like ChatGPT-4
In this article, we explore the application of principles and cognitive tests from the book “Thinking, Fast and Slow” by Nobel laureate Daniel Kahneman to evaluate the cognitive capabilities of ChatGPT-4.
13 Prompt Engineering Tips
This article delves into various strategies for prompt engineering, with the aim of helping users to optimize their interactions with AI, whether the goal is to seek information, create engaging content, or simply to foster an interesting dialogue.
smol ai developer: Text to Codebase
smol developer is a prototype of a junior developer agent that scaffolds an entire codebase for you once given a product specification.
OLTP vs OLAP: Key Differences, Use Cases, and Database Engine Overviews
In this article, we delve into an overview of OLTP and OLAP, explore their key differences, use cases, and offer insights into when one should be chosen over the other.
Getting Started with Weka
In this blog post, we’ll delve into a popular, free, and open-source toll called Weka, which is extensively used in data mining and machine learning tasks.
The Emergent Abilities of Large Language Models: Mirage or Milestone?
Emergent abilities, as the term suggests, are capabilities that emerge or manifest in Large Language Models (LLMs) as they scale up in size and complexity.
Understanding Bias in Data Science
Bias in data science is like a silent operator. It exists subtly in many forms, from data collection to interpretation, and if not checked, it can derail the objectivity and reliability of your analyses.