Towards AI

AUGUST 23, 2024

Hypothesis Testing <> Prompt Engineering Cycles Similar to hypothesis testing, prompt engineering cycles should include a detailed log of design choices, versions, performance gains, and the reasoning behind these choices, akin to a model development process. Like regular ML, LLM hyperparameters (e.g.,

ML 98

ML ML Hypothesis Testing Machine Learning 98

Towards AI

AUGUST 23, 2024

Simplifying LLM Development: Treat It Like Regular ML Photo by Daniel K Cheung on Unsplash Large Language Models (LLMs) are the latest buzz, often seen as both exciting and intimidating. Like regular ML, LLM hyperparameters (e.g., temperature or model version) should be logged as well.

ML 52

ML ML Hypothesis Testing Machine Learning 52

Pickl AI

NOVEMBER 28, 2024

Introduction Machine Learning ( ML ) is revolutionising industries, from healthcare and finance to retail and manufacturing. As businesses increasingly rely on ML to gain insights and improve decision-making, the demand for skilled professionals surges. This growth signifies Python’s increasing role in ML and related fields.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Dataconomy

AUGUST 22, 2023

Even if your plan on paper is quite simple, creating an algorithm from scratch is complicated and unpredictable, but luckily there are many mathematical theorems you can use for your next ML initiative. Cross-validation or other techniques can help with hyperparameter selection.

Machine Learning 91

Machine Learning Machine Learning Algorithm Deep Learning 91

Mlearning.ai

MAY 23, 2023

Let us first understand the meaning of bias and variance in detail: Bias: It is a kind of error in a machine learning model when an ML Algorithm is oversimplified. It is introduced into an ML Model when an ML algorithm is made highly complex. It further performs badly on the test data set. What is Cross-Validation?

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52