Presented By
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Make Data Work
March 25-28, 2019
San Francisco, CA
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Deep learning applications for non-engineers

Jeremy Howard ( fast.ai | USF | doc.ai and platform.ai)
11:00am–11:40am Wednesday, March 27, 2019
Data Science, Machine Learning & AI
 Location: 2016
Secondary topics:  AI and Data technologies in the cloud, Deep Learning, Open Data, Data Generation and Data Networks
Average rating: ****.
(4.80, 5 ratings)

Who is this presentation for?

  • Data scientists, machine learning researchers, aspiring data scientists, and product managers

Level

Beginner

Prerequisite knowledge

  • Basic knowledge of supervised machine learning

What you'll learn

  • Explore deep learning models that can be trained interactively by domain experts

Description

In recent history focus of the deep learning research community has been on infrastructure technologies like GPUs, optimization approaches, distributed training, etc. Now that state-of-the-art ImageNet networks can be trained at the speed of MNIST, we should look for ways to make deep learning-powered applications accessible to a broader community of users including non-engineers.

When non-engineers (that possess extensive domain expertise) can easily apply deep learning, it accelerates not only the pace of industry adoption but also the rate at which you uncover interesting and relevant research problems.

Jeremy Howard describes how to leverage the latest research from the deep learning and HCI communities to train neural networks from scratch—without code or preexisting labels. He then shares case studies in fashion, retail and ecommerce, travel, and agriculture where these approaches have been used.

Photo of Jeremy Howard

Jeremy Howard

fast.ai | USF | doc.ai and platform.ai

Jeremy Howard is an entrepreneur, business strategist, developer, and educator. Jeremy is a founding researcher at fast.ai, a research institute dedicated to making deep learning more accessible. He is also a Distinguished Research Scientist at the University of San Francisco, a faculty member at Singularity University, and a Young Global Leader with the World Economic Forum. Jeremy’s most recent startup, Enlitic, was the first company to apply deep learning to medicine and was selected one of the world’s top 50 smartest companies by MIT Tech Review two years running. Previously, he was the president and chief scientist at the data science platform Kaggle, where he was the top ranked participant in international machine learning competitions two years running; was the founding CEO of successful Australian startups FastMail and Optimal Decisions Group (acquired by Lexis-Nexis); and spent eight years in management consulting at McKinsey & Co. and AT Kearney. Jeremy has invested in, mentored, and advised many startups and contributed to many open source projects. He has made a number of television and video appearances, including as a regular guest on Australia’s highest-rated breakfast news program, a popular talk on TED.com, and data science and web development tutorials and discussions.