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Contenuto fornito da Hugo Bowne-Anderson. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Hugo Bowne-Anderson 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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Episode 31: Rethinking Data Science, Machine Learning, and AI

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Manage episode 428004512 series 3317544
Contenuto fornito da Hugo Bowne-Anderson. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Hugo Bowne-Anderson 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.

Hugo speaks with Vincent Warmerdam, a senior data professional and machine learning engineer at :probabl, the exclusive brand operator of scikit-learn. Vincent is known for challenging common assumptions and exploring innovative approaches in data science and machine learning.

In this episode, they dive deep into rethinking established methods in data science, machine learning, and AI. We explore Vincent's principled approach to the field, including:

  • The critical importance of exposing yourself to real-world problems before applying ML solutions
  • Framing problems correctly and understanding the data generating process
  • The power of visualization and human intuition in data analysis
  • Questioning whether algorithms truly meet the actual problem at hand
  • The value of simple, interpretable models and when to consider more complex approaches
  • The importance of UI and user experience in data science tools
  • Strategies for preventing algorithmic failures by rethinking evaluation metrics and data quality
  • The potential and limitations of LLMs in the current data science landscape
  • The benefits of open-source collaboration and knowledge sharing in the community

Throughout the conversation, Vincent illustrates these principles with vivid, real-world examples from his extensive experience in the field. They also discuss Vincent's thoughts on the future of data science and his call to action for more knowledge sharing in the community through blogging and open dialogue.

LINKS

Check out and subcribe to our lu.ma calendar for upcoming livestreams!

  continue reading

33 episodi

Artwork
iconCondividi
 
Manage episode 428004512 series 3317544
Contenuto fornito da Hugo Bowne-Anderson. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Hugo Bowne-Anderson 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.

Hugo speaks with Vincent Warmerdam, a senior data professional and machine learning engineer at :probabl, the exclusive brand operator of scikit-learn. Vincent is known for challenging common assumptions and exploring innovative approaches in data science and machine learning.

In this episode, they dive deep into rethinking established methods in data science, machine learning, and AI. We explore Vincent's principled approach to the field, including:

  • The critical importance of exposing yourself to real-world problems before applying ML solutions
  • Framing problems correctly and understanding the data generating process
  • The power of visualization and human intuition in data analysis
  • Questioning whether algorithms truly meet the actual problem at hand
  • The value of simple, interpretable models and when to consider more complex approaches
  • The importance of UI and user experience in data science tools
  • Strategies for preventing algorithmic failures by rethinking evaluation metrics and data quality
  • The potential and limitations of LLMs in the current data science landscape
  • The benefits of open-source collaboration and knowledge sharing in the community

Throughout the conversation, Vincent illustrates these principles with vivid, real-world examples from his extensive experience in the field. They also discuss Vincent's thoughts on the future of data science and his call to action for more knowledge sharing in the community through blogging and open dialogue.

LINKS

Check out and subcribe to our lu.ma calendar for upcoming livestreams!

  continue reading

33 episodi

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