Models and algorithms are the backbone of artificial intelligence. Join Michael Jolley as he explains how AI models, and specifically machine learning models work and how we classify them into groups like generative versus discriminative, classification and regression, or supervised learning, unsupervised learning, and reinforcement learning.

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00:00 - Introduction
00:08 - A Brief History of AI Models
00:39 - The Difference Between Algorithms and Models
01:57 - AI Models vs Machine Learning Models
02:58 - Supervised vs Unsupervised vs Reinforcement Learning Models
05:33 - Generative vs Discriminative Models
08:06 - Classification vs Regression Models

#artificialintelligence #ai #genai #aimodels #windows

Windows Developer

AI models autonomously make decisions or predictions without human intervention. They can be trained using algorithms which apply to inputs and generate desired outputs. Machine learning models, a subset of AI, improve their performance over time. AI models can be classified into supervised, unsupervised, and reinforcement learning based on their training methodologies. Additionally, models can be categorized as generative or discriminative, depending on their approach to predicting outputs. Models also vary by task, including classification or regression purposes, making them versatile for various applications from recommendation engines to natural language processing.

How Machine Learning Models Actually Work... the Easy Way

<p>Finally something I can show to my parents, so they stop believing that AI is just pure magic 😁</p>
