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How LLMs Are Actually Trained: Pre-Training vs. Post-Training Explained (with Julien Launay)

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867: LLMs and Agents Are Overhyped — with Dr. Andriy Burkov

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865: How to Grow (and Sell) a Data Science Consultancy — with Cal Al-Dhubaib

Super Data Science: ML & AI Podcast with Jon KrohnFeb 25, 2025

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863: TabPFN: Deep Learning for Tabular Data (That Actually Works!) — with Prof. Frank Hutter

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859: BAML: The Programming Language for AI — with Vaibhav Gupta

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797: Deep Learning Classics and Trends — with Dr. Rosanne Liu

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759: Full Encoder-Decoder Transformers Fully Explained — with Kirill Eremenko

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Super Data Science: ML & AI Podcast with Jon KrohnFeb 19, 2024

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758: The Mamba Architecture: Superior to Transformers in LLMs — with Jon Krohn (@JonKrohnLearns)

Super Data Science: ML & AI Podcast with Jon KrohnFeb 16, 2024

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747: Technical Intro to Transformers and LLMs — with Kirill Eremenko

Super Data Science: ML & AI Podcast with Jon KrohnJan 9, 2024

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When to Use XGBoost

Super Data Science: ML & AI Podcast with Jon KrohnDec 16, 2023

661: Designing Machine Learning Systems — with Chip Huyen

Super Data Science: ML & AI Podcast with Jon KrohnNov 25, 2023

What CLIP models are (Contrastive Language-Image Pre-training)

Super Data Science: ML & AI Podcast with Jon KrohnNov 23, 2023

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681: XGBoost: The Ultimate Classifier — with Matt Harrison

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657: How to Learn Data Engineering — with Andreas Kretz (@andreaskayy)

Super Data Science: ML & AI Podcast with Jon KrohnFeb 28, 2023

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Featured Results

919: Hopes and Fears of AGI, with All-Time Bestselling ML Author Aurélien Géron

913: LLM Pre-Training and Post-Training 101 — with Julien Launay

How LLMs Are Actually Trained: Pre-Training vs. Post-Training Explained (with Julien Launay)

885: Python Polars: The Definitive Guide — with Jeroen Janssens and Thijs Nieuwdorp

887: Multi-Agent Teams, Quantum Computing and the Future of Work — with Dell’s Global CTO John Roese

867: LLMs and Agents Are Overhyped — with Dr. Andriy Burkov

865: How to Grow (and Sell) a Data Science Consultancy — with Cal Al-Dhubaib

863: TabPFN: Deep Learning for Tabular Data (That Actually Works!) — with Prof. Frank Hutter

859: BAML: The Programming Language for AI — with Vaibhav Gupta

793: Bayesian Methods and Applications — with Alexandre Andorra

815: Polars: Faster DataFrame Ops — with Marco Gorelli

771: Gradient Boosting: XGBoost, LightGBM and CatBoost — with Kirill Eremenko

827: Polars: Past, Present and Future — with Polars Creator Ritchie Vink

797: Deep Learning Classics and Trends — with Dr. Rosanne Liu

Shiny: Build Robust Web Apps in Minutes with Python or R

What is Numba? The Python Compiler Explained

759: Full Encoder-Decoder Transformers Fully Explained — with Kirill Eremenko

The Perfect Pro Setup for Virtual Talks

Want a Great Presentation? Structure It (Here's How)

758: The Mamba Architecture: Superior to Transformers in LLMs — with Jon Krohn (@JonKrohnLearns)

747: Technical Intro to Transformers and LLMs — with Kirill Eremenko

When to Use XGBoost

661: Designing Machine Learning Systems — with Chip Huyen

What CLIP models are (Contrastive Language-Image Pre-training)

681: XGBoost: The Ultimate Classifier — with Matt Harrison

657: How to Learn Data Engineering — with Andreas Kretz (@andreaskayy)

Featured Results

919: Hopes and Fears of AGI, with All-Time Bestselling ML Author Aurélien Géron

913: LLM Pre-Training and Post-Training 101 — with Julien Launay

How LLMs Are Actually Trained: Pre-Training vs. Post-Training Explained (with Julien Launay)

885: Python Polars: The Definitive Guide — with Jeroen Janssens and Thijs Nieuwdorp

887: Multi-Agent Teams, Quantum Computing and the Future of Work — with Dell’s Global CTO John Roese

867: LLMs and Agents Are Overhyped — with Dr. Andriy Burkov

865: How to Grow (and Sell) a Data Science Consultancy — with Cal Al-Dhubaib

863: TabPFN: Deep Learning for Tabular Data (That Actually Works!) — with Prof. Frank Hutter

859: BAML: The Programming Language for AI — with Vaibhav Gupta

793: Bayesian Methods and Applications — with Alexandre Andorra

815: Polars: Faster DataFrame Ops — with Marco Gorelli

771: Gradient Boosting: XGBoost, LightGBM and CatBoost — with Kirill Eremenko

827: Polars: Past, Present and Future — with Polars Creator Ritchie Vink

797: Deep Learning Classics and Trends — with Dr. Rosanne Liu

Shiny: Build Robust Web Apps in Minutes with Python or R

What is Numba? The Python Compiler Explained

759: Full Encoder-Decoder Transformers Fully Explained — with Kirill Eremenko

The Perfect Pro Setup for Virtual Talks

Want a Great Presentation? Structure It (Here's How)

758: The Mamba Architecture: Superior to Transformers in LLMs — with Jon Krohn (@JonKrohnLearns)

747: Technical Intro to Transformers and LLMs — with Kirill Eremenko

When to Use XGBoost

661: Designing Machine Learning Systems — with Chip Huyen

What CLIP models are (Contrastive Language-Image Pre-training)

681: XGBoost: The Ultimate Classifier — with Matt Harrison

657: How to Learn Data Engineering — with Andreas Kretz (@andreaskayy)

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