The story of Fast.ai & why Python is not the future of ML with Jeremy Howard
Jeremy Howard is a founding researcher at fast.ai, a research institute dedicated to making Deep Learning more accessible. Previously, he was the CEO and Founder at Enlitic, an advanced machine learning company in San Francisco, California.
Howard is a faculty member at Singularity University, where he teaches data science. He is also a Young Global Leader with the World Economic Forum, and spoke at the World Economic Forum Annual Meeting 2014 on "Jobs For The Machines."
Howard advised Khosla Ventures as their Data Strategist, identifying the biggest opportunities for investing in data-driven startups and mentoring their portfolio companies to build data-driven businesses. Howard was the founding CEO of two successful Australian startups, FastMail and Optimal Decisions Group. Before that, he spent eight years in management consulting, at McKinsey & Company and AT Kearney.
TOPICS COVERED:
0:00 Introduction
0:52 Dad things
2:40 The story of Fast.ai
4:57 How the courses have evolved over time
9:24 Jeremy’s top down approach to teaching
13:02 From Fast.ai the course to Fast.ai the library
15:08 Designing V2 of the library from the ground up
21:44 The ingenious type dispatch system that powers Fast.ai
25:52 Were you able to realize the vision behind v2 of the library
28:05 Is it important to you that Fast.ai is used by everyone in the world, beyond the context of learning
29:37 Real world applications of Fast.ai, including animal husbandry
35:08 Staying ahead of the new developments in the field
38:50 A bias towards learning by doing
40:02 What’s next for Fast.ai
40:35 Python is not the future of Machine Learning
43:58 One underrated aspect of machine learning
45:25 Biggest challenge of machine learning in the real world
Links:
Deep learning R&D & education: http://fast.ai
Software: http://docs.fast.ai
Book: http://up.fm/book
Course: http://course.fast.ai
Papers:
The business impact of deep learning
https://dl.acm.org/doi/10.1145/2487575.2491127
De-identification Methods for Open Health Data
https://www.jmir.org/2012/1/e33/
Follow Jeremy on Twitter:
https://twitter.com/jeremyphoward
Visit our podcasts homepage for transcripts and more episodes!
www.wandb.com/podcast
🔊 Get our podcast on Soundcloud, Apple, and Spotify!
Soundcloud: https://bit.ly/2YnGjIq
Apple Podcasts: https://bit.ly/2WdrUvI
Spotify: https://bit.ly/2SqtadF
We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it!
👩🏼🚀Weights and Biases:
We’re always free for academics and open source projects. Email carey@wandb.com with any questions or feature suggestions.
- Blog: https://www.wandb.com/articles
- Gallery: See what you can create with W&B - https://app.wandb.ai/gallery
- Continue the conversation on our slack community - http://bit.ly/wandb-forum
🎙Host: Lukas Biewald - https://twitter.com/l2k
👩🏼💻Producer: Lavanya Shukla - https://twitter.com/lavanyaai
📹Editor: Cayla Sharp - http://caylasharp.com/
Видео The story of Fast.ai & why Python is not the future of ML with Jeremy Howard канала Weights & Biases
Howard is a faculty member at Singularity University, where he teaches data science. He is also a Young Global Leader with the World Economic Forum, and spoke at the World Economic Forum Annual Meeting 2014 on "Jobs For The Machines."
Howard advised Khosla Ventures as their Data Strategist, identifying the biggest opportunities for investing in data-driven startups and mentoring their portfolio companies to build data-driven businesses. Howard was the founding CEO of two successful Australian startups, FastMail and Optimal Decisions Group. Before that, he spent eight years in management consulting, at McKinsey & Company and AT Kearney.
TOPICS COVERED:
0:00 Introduction
0:52 Dad things
2:40 The story of Fast.ai
4:57 How the courses have evolved over time
9:24 Jeremy’s top down approach to teaching
13:02 From Fast.ai the course to Fast.ai the library
15:08 Designing V2 of the library from the ground up
21:44 The ingenious type dispatch system that powers Fast.ai
25:52 Were you able to realize the vision behind v2 of the library
28:05 Is it important to you that Fast.ai is used by everyone in the world, beyond the context of learning
29:37 Real world applications of Fast.ai, including animal husbandry
35:08 Staying ahead of the new developments in the field
38:50 A bias towards learning by doing
40:02 What’s next for Fast.ai
40:35 Python is not the future of Machine Learning
43:58 One underrated aspect of machine learning
45:25 Biggest challenge of machine learning in the real world
Links:
Deep learning R&D & education: http://fast.ai
Software: http://docs.fast.ai
Book: http://up.fm/book
Course: http://course.fast.ai
Papers:
The business impact of deep learning
https://dl.acm.org/doi/10.1145/2487575.2491127
De-identification Methods for Open Health Data
https://www.jmir.org/2012/1/e33/
Follow Jeremy on Twitter:
https://twitter.com/jeremyphoward
Visit our podcasts homepage for transcripts and more episodes!
www.wandb.com/podcast
🔊 Get our podcast on Soundcloud, Apple, and Spotify!
Soundcloud: https://bit.ly/2YnGjIq
Apple Podcasts: https://bit.ly/2WdrUvI
Spotify: https://bit.ly/2SqtadF
We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it!
👩🏼🚀Weights and Biases:
We’re always free for academics and open source projects. Email carey@wandb.com with any questions or feature suggestions.
- Blog: https://www.wandb.com/articles
- Gallery: See what you can create with W&B - https://app.wandb.ai/gallery
- Continue the conversation on our slack community - http://bit.ly/wandb-forum
🎙Host: Lukas Biewald - https://twitter.com/l2k
👩🏼💻Producer: Lavanya Shukla - https://twitter.com/lavanyaai
📹Editor: Cayla Sharp - http://caylasharp.com/
Видео The story of Fast.ai & why Python is not the future of ML with Jeremy Howard канала Weights & Biases
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
The wonderful and terrifying implications of computers that can learn | Jeremy HowardHow to win Kaggle competitions with Anthony GoldbloomJeremy Howard: Deep Learning Frameworks - TensorFlow, PyTorch, fast.ai | AI Podcast ClipsBringing genetic insights to everyone with Invitae's Head of AI, Matthew DavisThe first 20 hours -- how to learn anything | Josh Kaufman | TEDxCSULesson 1 - Deep Learning for Coders (2020)nbdev tutorialfast.ai's Jeremy Howard on Why Python is not the future of machine learning - Gradient Dissent ClipFast.ai, AutoML, and Software Engineering for ML // Jeremy Howard // Coffee Session #47Elon Musk: Neuralink, AI, Autopilot, and the Pale Blue Dot | Lex Fridman Podcast #49Andrew Ng: Deep Learning, Education, and Real-World AI | Lex Fridman Podcast #73A.I. teaches itself to drive in TrackmaniaThe 7 Reasons Most Machine Learning Funds Fail Marcos Lopez de Prado from QuantCon 201820 Years of Tech Startup Experiences in One HourKaggle Tips for Feature Engineering and Selection | by Gilberto Titericz | Kaggle Days Meetup MadridHow Well Can an AI Learn Physics? ⚛This Short Film Is Written Entirely By AIPractical Deep Learning for Coders - Full Course from fast.ai and Jeremy HowardMost Research in Deep Learning is a Total Waste of Time - Jeremy Howard | AI Podcast Clips