Jeremy Howard — The Story of fast.ai and Why Python Is Not the Future of ML

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 Introduction0:52 Dad things2:40 The story of Fast.ai4:57 How the courses have evolved over time9:24 Jeremy’s top down approach to teaching13:02 From Fast.ai the course to Fast.ai the library15:08 Designing V2 of the library from the ground up21:44 The ingenious type dispatch system that powers Fast.ai25:52 Were you able to realize the vision behind v2 of the library28:05 Is it important to you that Fast.ai is used by everyone in the world, beyond the context of learning29:37 Real world applications of Fast.ai, including animal husbandry35:08 Staying ahead of the new developments in the field38:50 A bias towards learning by doing40:02 What’s next for Fast.ai40.35 Python is not the future of Machine Learning43:58 One underrated aspect of machine learning45:25 Biggest challenge of machine learning in the real worldFollow Jeremy on Twitter:https://twitter.com/jeremyphowardLinks:Deep learning R&D & education: http://fast.aiSoftware: http://docs.fast.aiBook: http://up.fm/bookCourse: http://course.fast.aiPapers:The business impact of deep learninghttps://dl.acm.org/doi/10.1145/2487575.2491127De-identification Methods for Open Health Datahttps://www.jmir.org/2012/1/e33/Visit our podcasts homepage for transcripts and more episodes!www.wandb.com/podcast🔊 Get our podcast on Soundcloud, Apple, and Spotify!YouTube: https://www.youtube.com/c/WeightsBiasesApple Podcasts: https://bit.ly/2WdrUvISpotify: https://bit.ly/2SqtadFWe 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 [email protected] 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/