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

Follow Jeremy on Twitter:

https://twitter.com/jeremyphoward

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/

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