Episode notes
At inference, large language models use in-context learning with zero-, one-, or few-shot examples to perform new tasks without weight updates, and can be grounded with Retrieval Augmented Generation (RAG) by embedding documents into vector databases for real-time factual lookup using cosine similarity. LLM agents autonomously plan, act, and use external tools via orchestrated loops with persistent memory, while recent benchmarks like GPQA (STEM reasoning), SWE Bench (agentic coding), and MMMU (multimodal college-level tasks) test performance alongside prompt engineering techniques such as…
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Machine learning audio course, teaching the fundamentals of machine learning and artificial intelligence. It covers intuition, models (shallow and deep), math, languages, frameworks, etc. Where your other ML resources provide the trees, I provide the forest. Consider MLG your syllabus, with highly-c
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S1 · E64 · 22 Feb 2026 · 34 min
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S1 · E62 · 12 Jul 2025 · 32 min
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S1 · E61 · 9 Jul 2025 · 31 min
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S1 · E60 · 30 May 2025 · 1 hr 6 min
MLG 036 Autoencoders
Auto encoders are neural networks that compress data into a smaller "code," enabling dimensionality reduction, data cleaning, and lossy compression by reconstructing original inputs from this code. Advanced auto encoder types, such as denoising, sparse, and variational auto encoders, extend these concepts for applications in generative modeling, interpretability, and synthetic data generation. Links Notes and resources at ocdevel.com/mlg/36 Try a walking desk - stay healthy & sharp while you learn & code Build the future of multi-agent software with AGNTCY . Thanks to T.J. Wilder from…
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S1 · E58 · 7 May 2025 · 51 min
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S1 · E57 · 13 Apr 2025 · 33 min
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S1 · E56 · 13 Apr 2025 · 34 min
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S1 · E55 · 9 Feb 2025 · 28 min
MLA 022 Vibe Coding: Codex vs Claude Code vs Antigravity vs Grok
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S1 · E54 · 9 Feb 2025 · 43 min
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