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Andriy Burkov

Author of best-selling machine learning books and AI influencer. He is the Machine Learning Lead at Talent Neuron and runs his own book publishing company.

Top 3 podcasts with Andriy Burkov

Ranked by the Snipd community
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100 snips
Apr 14, 2025 • 1h 5min

#297 The Past and Future of Language Models with Andriy Burkov, Author of The Hundred-Page Machine Learning Book

Andriy Burkov, author of influential AI books and Machine Learning Lead at TalentNeuron, dives into the fascinating world of language models. He dispels common misconceptions about AI, clarifying that it’s a collection of algorithms rather than a singular entity. Andriy explores the historical significance of traditional AI algorithms and the evolving landscape of language models, including the rise of transformers. He also addresses the limitations of AI in specialized fields and shares tips on effective coding tools that merge with AI for enhanced productivity.
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86 snips
Mar 4, 2025 • 1h 33min

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

Dr. Andriy Burkov, a best-selling author and AI influencer, shares his insights on the future of AI, particularly questioning the hype around AI agents and large language models. He discusses innovative chatbot designs that avoid common pitfalls like hallucination. Burkov also reflects on the journey of language modeling, the evolution of natural language processing, and how Talent Neuron leverages data to transform talent management. He emphasizes the gap between human cognitive abilities and AI, stressing the skepticism around the effectiveness of AI in real-world applications.
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18 snips
Apr 11, 2025 • 31min

878: In Case You Missed It in March 2025

Andriy Burkov, an acclaimed author and AI developer, teams up with Varun Godbole, a former Google engineer who contributed to the Gemini model. They dive into artificial general intelligence, discussing the cognitive skills of animals and their implications for AI development. The conversation highlights a tuning playbook for neural networks, emphasizing the challenges of hyperparameter tuning. They also explore the need for human connection in AI and innovative approaches for enhancing machine learning education and experimentation.