Learning from Machine Learning

This episode we welcome Sebastian Raschka, Lead AI Educator at Lightning and author of Machine Learning with Pytorch and Scikit-Learn to discuss the best ways to learn machine learning, his open source work, how to use chatGPT, AGI, responsible AI and so much more. Sebastian is a fountain of knowledge and it was a pleasure to get his insights on this fast moving industry. Learning from Machine Learning, a podcast that explores more than just algorithms and data: Life lessons from the experts. Resources to learn more about Sebastian Raschka and his work:

https://sebastianraschka.com/

https://lightning.ai/

Machine Learning with Pytorch and Scikit-Learn

Machine Learning Q and AI

Resources to learn more about Learning from Machine Learning and the host: https://www.linkedin.com/company/learning-from-machine-learning

https://www.linkedin.com/in/sethplevine/

https://medium.com/@levine.seth.p

twitter

References from Episode

https://scikit-learn.org/stable/

http://rasbt.github.io/mlxtend/

https://github.com/BioPandas/biopandas

Understanding and Coding the Self-Attention Mechanism of Large Language Models From Scratch

Andrew Ng - https://www.andrewng.org/

Andrej Karpathy - https://karpathy.ai/

Paige Bailey - https://github.com/dynamicwebpaige

Contents

01:15 - Career Background

05:18 - Industry vs. Academia

08:18 - First Project in ML

15:04 - Open Source Projects Involvement

20:00 - Machine Learning: Q&AI

24:18 - ChatGPT as Brainstorm Assistant

25:38 - Hype vs. Reality

27:55 - AGI

31:00 - Use Cases for Generative Models

34:01 - Should the goal to be to replicate human intelligence?

39:18 - Delegating Tasks using LLM

42:26 - ML Models are overconfident on Out of Distribution

44:54 - Responsible AI and ML

45:59 - Complexity of ML Systems

47:26 - Trend for ML Practitioners to move to AI Ethics

49:27 - What advice would you give to someone just starting out?

52:20 - Advice that you’ve received that has helped you

54:08 - Andrew Ng Advice

55:20 - Exercise of Implementing Algorithms from Scratch

59:00 - Who else has influenced you?

01:01:18 - Production and Real-World Applications - Don’t reinvent the wheel

01:03:00 - What has a career in ML taught you about life?

This episode welcomes Nils Reimers, Director of Machine Learning at Cohere and former research at Hugging Face, to discuss Natural Language Processing, Sentence Transformers and the future of Machine Learning. Nils is best known as the creator of Sentence Transformers, a powerful framework for generating high-quality sentence embeddings that has become increasingly popular in the ML community with over 9K stars on Github. With Sentence Transformers, Nils has enabled researchers and developers (including me) to train state-of-the-art models for a wide range of NLP tasks, including text classification, semantic similarity, and question-answering. His contributions have been recognized by numerous awards and publications in top-tier conferences and journals.

Resources to learn more about Nils Reimers and his work:

https://www.nils-reimers.de/

https://www.sbert.net/

https://scholar.google.com/citations?...

https://cohere.ai/

Resources to learn more about Learning from Machine Learning:

https://www.linkedin.com/company/learning-from-machine-learning

https://www.linkedin.com/in/sethplevine/

https://medium.com/@levine.seth.p

Youtube Clips

02:29 What attracted you to Machine Learning?

06:32 What is sentence transformers?

28:02 Benchmarks and P-Hacking

33:53 What’s an important question that remains unanswered in Machine Learning?

38:41 How do you view the gap between the hype and the reality in Machine Learning?

50:45 What advice would you give to someone just starting out?

52:30 What advice would you give yourself when you were just starting out in your career?

57:22 What has a career in ML taught you about life?

Learning from Machine Learning, a podcast that explores more than just algorithms and data: Life lessons from the experts. This episode we welcome Vincent Warmerdam, creator of calmcode, and machine learning engineer at SpaCy to discuss Data Science, models and much more. @learningfrommachinelearning

Resources to learn more about Vincent Warmerdam:

https://calmcode.io/

https://youtu.be/kYMfE9u-lMo

https://youtu.be/S7vhi6RjBZA

https://github.com/koaning

References from the Episode:

You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It's Making the World a Weirder Place https://amzn.to/3Jt1qjX

The Future of Operational Research is Past https://ackoffcenter.blogs.com/files/the-future-of-operational-research-is-past.pdf

Supervised Learning is great - it's data collection that's broken https://explosion.ai/blog/supervised-learning-data-collection

Deon - An ethics checklist for data scientists https://deon.drivendata.org/

Hadley Wickham - https://hadley.nz/

Katharine Jarmul - https://www.linkedin.com/in/katharinejarmul/?originalSubdomain=de

Vicki Boykis - https://vickiboykis.com/

Brett Victor - https://youtu.be/8pTEmbeENF4

Resources to learn more about Learning from Machine Learning:

https://www.linkedin.com/company/learning-from-machine-learning/

https://www.linkedin.com/in/sethplevine/

https://medium.com/@levine.seth.p

The inaugural episode of Learning from Machine Learning, a podcast that explores more than just algorithms and data: Life lessons from the experts.

This episode we welcome Maarten Grootendorst to discuss BERTopic, Data Science, Psychology and the future of Machine Learning and Natural Language Processing.

Towards Data Science Article featuring this interview

Resources to learn more about Maarten Grootendorst:

https://www.maartengrootendorst.com/

https://maartengr.github.io/BERTopic/

https://www.linkedin.com/in/mgrootendorst/

https://twitter.com/MaartenGr

https://medium.com/@maartengrootendorst

Resources to learn more about Learning from Machine Learning:

https://www.linkedin.com/company/learning-from-machine-learning/

https://www.linkedin.com/in/sethplevine/

https://medium.com/@levine.seth.p