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Tokenization & Probability: How LLMs Pick the Next Word | Finale
THE GRAND FINALE of The Math Behind AI. You type a question. ChatGPT answers. In this final episode, we trace the ENTIRE pipeline from your keyboard to the model's response: tokenization (Byte Pair Encoding), embeddings, positional encodings, transformer blocks, softmax, temperature sampling, and autoregressive generation. Everything from Episodes 1 to 9 comes together. By the end, you'll understand exactly how ChatGPT, Claude, and every modern LLM actually works.
🎯 CHAPTERS
00:00 You type, ChatGPT answers — how?
1:00 Tokenization: Byte Pair Encoding (BPE)
4:00 Walking through "The cat sat on the" step by step
5:30 Embed → positional encode → transformer blocks
7:30 Sampling the next token (greedy vs temperature)
10:00 Autoregressive generation: one word at a time
11:30 How the model is actually trained (next-token prediction)
13:30 The complete series recap (Episodes 1 through 10)
16:00 What you now understand about AI
📌 KEY CONCEPTS
• Byte Pair Encoding (BPE) and tokenization
• The full LLM pipeline: tokens → embeddings → transformer → softmax
• Positional encoding (order matters)
• Greedy decoding vs temperature sampling
• Top-k and top-p (nucleus) sampling
• Autoregressive generation
• Next-token prediction training objective
• Series recap: regression → gradient descent → neural networks → backprop → loss → activation → transformers → embeddings → softmax → LLMs
📚 PREREQUISITES
All 9 previous episodes! If you've followed the series, you have the full picture now.
📚 THE SERIES — COMPLETE
This is the final episode of The Math Behind AI. If you've watched all 10, you now understand the mathematical foundations of modern AI better than 99% of people who use it every day. Thank you for watching.
📱 WANT TO PRACTICE THE MATH?
NovaMaths — SAT & ACT Math prep with 749+ exercises:
https://www.novamaths.app
🌍 French channel: @MathsAcademy27
─────────────────────
Channel hosted by Julien, certified math teacher with 30 years of classroom experience.
#Tokenization #BPE #LLM #ChatGPT #AIMath
Видео Tokenization & Probability: How LLMs Pick the Next Word | Finale канала Math Vision
🎯 CHAPTERS
00:00 You type, ChatGPT answers — how?
1:00 Tokenization: Byte Pair Encoding (BPE)
4:00 Walking through "The cat sat on the" step by step
5:30 Embed → positional encode → transformer blocks
7:30 Sampling the next token (greedy vs temperature)
10:00 Autoregressive generation: one word at a time
11:30 How the model is actually trained (next-token prediction)
13:30 The complete series recap (Episodes 1 through 10)
16:00 What you now understand about AI
📌 KEY CONCEPTS
• Byte Pair Encoding (BPE) and tokenization
• The full LLM pipeline: tokens → embeddings → transformer → softmax
• Positional encoding (order matters)
• Greedy decoding vs temperature sampling
• Top-k and top-p (nucleus) sampling
• Autoregressive generation
• Next-token prediction training objective
• Series recap: regression → gradient descent → neural networks → backprop → loss → activation → transformers → embeddings → softmax → LLMs
📚 PREREQUISITES
All 9 previous episodes! If you've followed the series, you have the full picture now.
📚 THE SERIES — COMPLETE
This is the final episode of The Math Behind AI. If you've watched all 10, you now understand the mathematical foundations of modern AI better than 99% of people who use it every day. Thank you for watching.
📱 WANT TO PRACTICE THE MATH?
NovaMaths — SAT & ACT Math prep with 749+ exercises:
https://www.novamaths.app
🌍 French channel: @MathsAcademy27
─────────────────────
Channel hosted by Julien, certified math teacher with 30 years of classroom experience.
#Tokenization #BPE #LLM #ChatGPT #AIMath
Видео Tokenization & Probability: How LLMs Pick the Next Word | Finale канала Math Vision
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8 мая 2026 г. 12:30:32
00:06:33
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