20:28
The physics behind diffusion models
25:07
Reverse-engineering GGUF | Post-Training Quantization
24:08
Training models with only 4 bits | Fully-Quantized Training
24:37
The myth of 1-bit LLMs | Quantization-Aware Training
20:34
How LLMs survive in low precision | Quantization Fundamentals
16:04
Knowledge Distillation: How LLMs train each other
26:42
Mixture of Experts: How LLMs get bigger without getting slower
24:02
Llama 4 Explained: Architecture, Long Context, and Native Multimodality
23:16
DeepSeek's GRPO (Group Relative Policy Optimization) | Reinforcement Learning for LLMs
22:03
Proximal Policy Optimization (PPO) for LLMs Explained Intuitively
13:49
TΓΌlu 3 from AI2: Full open-source fine-tuning recipe for LLMs
15:53
8 Timeless tips for training LLMs | Become a better ML engineer
09:55
How does OpenAI Operator work under the hood? | Tech deep dive
15:29
How does DeepSeek actually work? | Full technical review
09:53
Discover How LLMs Work by Dissecting Llama
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About
AI explainer videos from a former Google Research engineer, now startup founder. Expect American optimism with a solid dose of Eastern European cynicism.
Find (free) supplemental materials (slide decks, paper reading lists, Colab notebooks) on Patreon π www.patreon.com/c/JuliaTurc
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