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Making Machine Learning more accessible | Sebastian Raschka

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Manage episode 351051121 series 2859018
Treść dostarczona przez Jay Shah. Cała zawartość podcastów, w tym odcinki, grafika i opisy podcastów, jest przesyłana i udostępniana bezpośrednio przez Jay Shah lub jego partnera na platformie podcastów. Jeśli uważasz, że ktoś wykorzystuje Twoje dzieło chronione prawem autorskim bez Twojej zgody, możesz postępować zgodnie z procedurą opisaną tutaj https://pl.player.fm/legal.

Sebastian Raschka​ is the lead AI educator at GridAI. He is the author of the book "Machine Learning with PyTorch and Scikit Learn" and also a few other books that cover the fundamentals of #machinelearning and #deeplearning techniques and implementing them with Python. He is also an Assistant Professor of Statistics at the University of Wisconsin-Madison and has been actively involved in making ML more accessible to beginners through his blogs, video tutorials, tweets and of course his books. He also holds a doctorate in Computational and Quantitative Biology from Michigan State University.
Time Stamps of the Podcast
00:00:00 Introductions
00:02:40 Entry point in AI/ML that made you interested in it
00:05:30 How did you go about learning the basics and implementation of various methods?
00:11:45 What makes Python ideal for learning Machine Learning recently?
00:21:54 What is your book about and who is this for?
00:33:55 What goes into writing a good technical book?
00:40:50 Applying ML to toy datasets vs real-world research problems
00:47:40 Choosing b/w machine learning methods & deep learning methods
00:56:22 Large models vs architecture efficient models
01:01:25 Interpretability & Explainability in AI
01:08:45 Insights for people interested in machine learning research, academia or PhD
01:14:17 Keeping up with research in deep learning
Sebastian's homepage: https://sebastianraschka.com/
Twitter: https://mobile.twitter.com/rasbt
LinkedIn: https://www.linkedin.com/in/sebastianraschka/
His book: https://www.amazon.com/Machine-Learning-PyTorch-Scikit-Learn-scikit-learn-ebook-dp-B09NW48MR1/dp/B09NW48MR1/
Video Tutorials: @SebastianRaschka
About the Host:
Jay is a Ph.D. student at Arizona State University.
Linkedin: https://www.linkedin.com/in/shahjay22/
Twitter: https://twitter.com/jaygshah22
Reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***

Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml
About the author: https://www.public.asu.edu/~jgshah1/

  continue reading

92 odcinków

Artwork
iconUdostępnij
 
Manage episode 351051121 series 2859018
Treść dostarczona przez Jay Shah. Cała zawartość podcastów, w tym odcinki, grafika i opisy podcastów, jest przesyłana i udostępniana bezpośrednio przez Jay Shah lub jego partnera na platformie podcastów. Jeśli uważasz, że ktoś wykorzystuje Twoje dzieło chronione prawem autorskim bez Twojej zgody, możesz postępować zgodnie z procedurą opisaną tutaj https://pl.player.fm/legal.

Sebastian Raschka​ is the lead AI educator at GridAI. He is the author of the book "Machine Learning with PyTorch and Scikit Learn" and also a few other books that cover the fundamentals of #machinelearning and #deeplearning techniques and implementing them with Python. He is also an Assistant Professor of Statistics at the University of Wisconsin-Madison and has been actively involved in making ML more accessible to beginners through his blogs, video tutorials, tweets and of course his books. He also holds a doctorate in Computational and Quantitative Biology from Michigan State University.
Time Stamps of the Podcast
00:00:00 Introductions
00:02:40 Entry point in AI/ML that made you interested in it
00:05:30 How did you go about learning the basics and implementation of various methods?
00:11:45 What makes Python ideal for learning Machine Learning recently?
00:21:54 What is your book about and who is this for?
00:33:55 What goes into writing a good technical book?
00:40:50 Applying ML to toy datasets vs real-world research problems
00:47:40 Choosing b/w machine learning methods & deep learning methods
00:56:22 Large models vs architecture efficient models
01:01:25 Interpretability & Explainability in AI
01:08:45 Insights for people interested in machine learning research, academia or PhD
01:14:17 Keeping up with research in deep learning
Sebastian's homepage: https://sebastianraschka.com/
Twitter: https://mobile.twitter.com/rasbt
LinkedIn: https://www.linkedin.com/in/sebastianraschka/
His book: https://www.amazon.com/Machine-Learning-PyTorch-Scikit-Learn-scikit-learn-ebook-dp-B09NW48MR1/dp/B09NW48MR1/
Video Tutorials: @SebastianRaschka
About the Host:
Jay is a Ph.D. student at Arizona State University.
Linkedin: https://www.linkedin.com/in/shahjay22/
Twitter: https://twitter.com/jaygshah22
Reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***

Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml
About the author: https://www.public.asu.edu/~jgshah1/

  continue reading

92 odcinków

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