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Contenuto fornito da Alexandre Andorra. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Alexandre Andorra o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.
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#122 Learning and Teaching in the Age of AI, with Hugo Bowne-Anderson

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Manage episode 457627039 series 2635823
Contenuto fornito da Alexandre Andorra. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Alexandre Andorra o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!


Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

  • Effective data science education requires feedback and rapid iteration.
  • Building LLM applications presents unique challenges and opportunities.
  • The software development lifecycle for AI differs from traditional methods.
  • Collaboration between data scientists and software engineers is crucial.
  • Hugo's new course focuses on practical applications of LLMs.
  • Continuous learning is essential in the fast-evolving tech landscape.
  • Engaging learners through practical exercises enhances education.
  • POC purgatory refers to the challenges faced in deploying LLM-powered software.
  • Focusing on first principles can help overcome integration issues in AI.
  • Aspiring data scientists should prioritize problem-solving over specific tools.
  • Engagement with different parts of an organization is crucial for data scientists.
  • Quick paths to value generation can help gain buy-in for data projects.
  • Multimodal models are an exciting trend in AI development.
  • Probabilistic programming has potential for future growth in data science.
  • Continuous learning and curiosity are vital in the evolving field of data science.

Chapters:

09:13 Hugo's Journey in Data Science and Education

14:57 The Appeal of Bayesian Statistics

19:36 Learning and Teaching in Data Science

24:53 Key Ingredients for Effective Data Science Education

28:44 Podcasting Journey and Insights

36:10 Building LLM Applications: Course Overview

42:08 Navigating the Software Development Lifecycle

48:06 Overcoming Proof of Concept Purgatory

55:35 Guidance for Aspiring Data Scientists

01:03:25 Exciting Trends in Data Science and AI

01:10:51 Balancing Multiple Roles in Data Science

01:15:23 Envisioning Accessible Data Science for All

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim

  continue reading

182 episodi

Artwork
iconCondividi
 
Manage episode 457627039 series 2635823
Contenuto fornito da Alexandre Andorra. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Alexandre Andorra o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!


Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

  • Effective data science education requires feedback and rapid iteration.
  • Building LLM applications presents unique challenges and opportunities.
  • The software development lifecycle for AI differs from traditional methods.
  • Collaboration between data scientists and software engineers is crucial.
  • Hugo's new course focuses on practical applications of LLMs.
  • Continuous learning is essential in the fast-evolving tech landscape.
  • Engaging learners through practical exercises enhances education.
  • POC purgatory refers to the challenges faced in deploying LLM-powered software.
  • Focusing on first principles can help overcome integration issues in AI.
  • Aspiring data scientists should prioritize problem-solving over specific tools.
  • Engagement with different parts of an organization is crucial for data scientists.
  • Quick paths to value generation can help gain buy-in for data projects.
  • Multimodal models are an exciting trend in AI development.
  • Probabilistic programming has potential for future growth in data science.
  • Continuous learning and curiosity are vital in the evolving field of data science.

Chapters:

09:13 Hugo's Journey in Data Science and Education

14:57 The Appeal of Bayesian Statistics

19:36 Learning and Teaching in Data Science

24:53 Key Ingredients for Effective Data Science Education

28:44 Podcasting Journey and Insights

36:10 Building LLM Applications: Course Overview

42:08 Navigating the Software Development Lifecycle

48:06 Overcoming Proof of Concept Purgatory

55:35 Guidance for Aspiring Data Scientists

01:03:25 Exciting Trends in Data Science and AI

01:10:51 Balancing Multiple Roles in Data Science

01:15:23 Envisioning Accessible Data Science for All

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim

  continue reading

182 episodi

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