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

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 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/



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/WeightsBiases

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/

Tämä jakso on lisätty Podme-palveluun avoimen RSS-syötteen kautta eikä se ole Podmen omaa tuotantoa. Siksi jakso saattaa sisältää mainontaa.

Jaksot(136)

Adrien Treuille — Building Blazingly Fast Tools That People Love

Adrien Treuille — Building Blazingly Fast Tools That People Love

Adrien shares his journey from making games that advance science (Eterna, Foldit) to creating a Streamlit, an open-source app framework enabling ML/Data practitioners to easily build powerful and inte...

4 Joulu 202045min

Peter Norvig – Singularity Is in the Eye of the Beholder

Peter Norvig – Singularity Is in the Eye of the Beholder

We're thrilled to have Peter Norvig join us to talk about the evolution of deep learning, his industry-defining book, his work at Google, and what he thinks the future holds for machine learning resea...

20 Marras 202047min

Robert Nishihara — The State of Distributed Computing in ML

Robert Nishihara — The State of Distributed Computing in ML

The story of Ray and what lead Robert to go from reinforcement learning researcher to creating open-source tools for machine learning and beyondRobert is currently working on Ray, a high-performance d...

13 Marras 202035min

Ines & Sofie — Building Industrial-Strength NLP Pipelines

Ines & Sofie — Building Industrial-Strength NLP Pipelines

Sofie and Ines walk us through how the new spaCy library helps build end to end SOTA natural language processing workflows.Ines Montani is the co-founder of Explosion AI, a digital studio specializing...

29 Loka 202058min

Daeil Kim — The Unreasonable Effectiveness of Synthetic Data

Daeil Kim — The Unreasonable Effectiveness of Synthetic Data

Supercharging computer vision model performance by generating years of training data in minutes.Daeil Kim is the co-founder and CEO of AI.Reverie(https://aireverie.com/), a startup that specializes in...

15 Loka 202037min

Joaquin Candela — Definitions of Fairness

Joaquin Candela — Definitions of Fairness

Joaquin chats about scaling and democratizing AI at Facebook, while understanding fairness and algorithmic bias.---Joaquin Quiñonero Candela is Distinguished Tech Lead for Responsible AI at Facebook, ...

1 Loka 20201h 19min

Richard Socher — The Challenges of Making ML Work in the Real World

Richard Socher — The Challenges of Making ML Work in the Real World

Richard Socher, ex-Chief Scientist at Salesforce, joins us to talk about The AI Economist, NLP protein generation and biggest challenge in making ML work in the real world.Richard Socher was the Chief...

29 Syys 202050min

Zack Chase Lipton — The Medical Machine Learning Landscape

Zack Chase Lipton — The Medical Machine Learning Landscape

How Zack went from being a musician to professor, how medical applications of Machine Learning are developing, and the challenges of counteracting bias in real world applications.Zachary Chase Lipton ...

17 Syys 202059min

Suosittua kategoriassa Liike-elämä ja talous

sijotuskasti
psykopodiaa-podcast
rss-rahapodi
mimmit-sijoittaa
rss-oivalluksia-rahasta-elamasta
rss-rahamania
rss-sami-miettinen-neuvottelija
rss-startup-ministerio
asuntoasiaa-paivakirjat
rss-lahtijat
rahapuhetta
sijoituspodi
hyva-paha-johtaminen
rss-kaikki-koroista
rss-bisnesta-bebeja
rss-karon-grilli
rss-lentopaivakirjat
rss-set-for-life-sijoita-ja-vaurastu
rss-h-asselmoilanen
rss-paivystyspodi