6:10
"Attention Weights: How Transformers Decide What to Focus On"
8:49
Attention Is All You Need Paper Explained | Transformer Architecture Demo
6:16
History of AI Language Models: From Rulebooks to Transformers Explained
2:26
Gradient Descent Explained: Walking Downhill Analogy for Beginners
2:46
Backpropagation Explained for Beginners: How AI Learns From Its Mistakes
3:05
Vector Space in AI Explained: The Cafeteria Analogy
2:19
Next Character Prediction: How AI Learns to Write
Neural Network Explained for Beginners (Brain, Factory & Volume Knob Analogy)
3:12
What Is a Tensor in AI? Beginner-Friendly Explanation with Examples
3:29
Transformer Hyperparameters Explained: batch_size, block_size, n_embed
2:32
Embeddings in AI Explained for Beginners | Library, Dartboard & Magnet Analogies + Demo
0:59
How AI Predicts the Next Word: Beginner's Guide with Autocomplete Demo
1:12
Tokenization Explained: Word vs Character Tokens (Pizza Analogy Demo)
Tokenization Explained: How Computers Read Text (Beginner NLP Tutorial)
