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AI & Machine Learning

Artificial intelligence concepts from RAG to Neural Networks, explained with simple analogies.

32 concepts • Click Quick for a 2-minute summary, or Deep Dive for comprehensive learning.

32 concepts found
📚

RAG

An open-book exam for AI

intermediate
📍

Embeddings

GPS coordinates for words

intermediate
🧠

LLMs

A very well-read librarian

beginner
🤖

Transformers

Speed readers with strong focus

advanced
👀

Attention

Highlighting the important words

advanced
✍️

Prompt Engineering

Asking questions the right way

beginner
🎻

Fine-tuning

Teaching an expert new tricks

advanced
🤝

AI Agents

Personal assistants that take action

intermediate
🕸️

Neural Networks

Brain cells learning together

intermediate
💬

NLP

Teaching computers to read and write

intermediate
🧩

Tokens

Breaking text into puzzle pieces

beginner
🪟

Context Window

How much an AI can remember at once

beginner
🌡️

Temperature

How creative vs predictable AI answers

beginner
👻

Hallucinations

When AI confidently makes things up

beginner
📈

Machine Learning

Teaching computers to learn from examples

beginner
🧬

Deep Learning

Neural networks with many layers of understanding

intermediate
👨‍🏫

Supervised Learning

Learning from labeled examples with a teacher

beginner
🔎

Unsupervised Learning

Finding hidden patterns without labels

intermediate
🎮

Reinforcement Learning

Learning by trial, error, and rewards

advanced
🖼️

CNN

Neural networks that see patterns in images

intermediate
🔄

RNN

Neural networks with memory for sequences

intermediate
🎨

GAN

Two networks competing to create realistic content

advanced
🌫️

Diffusion Models

Creating images by removing noise step by step

advanced
🎬

Multimodal AI

AI that understands text, images, and audio together

intermediate
👁️

Computer Vision

Teaching computers to understand images

intermediate
🎤

Speech Recognition

Converting spoken words to text

intermediate
😊

Sentiment Analysis

Detecting emotions and opinions in text

beginner
🏋️

Model Training

Feeding data to teach AI models

intermediate
🔮

Inference

Using trained models to make predictions

beginner
📝

Overfitting

When AI memorizes instead of learns

intermediate
⚖️

Bias in AI

When AI learns unfair patterns from data

beginner
🔀

Transfer Learning

Using knowledge from one task for another

intermediate