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

Weights & Biases · Beginner ·📐 ML Fundamentals ·5y ago
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
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1 0. What is machine learning?
0. What is machine learning?
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 2 1. Build Your First Machine Learning Model
1. Build Your First Machine Learning Model
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 3 Intro to ML: Course Overview
Intro to ML: Course Overview
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 4 2. Multi-Layer Perceptrons
2. Multi-Layer Perceptrons
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 5 3. Convolutional Neural Networks
3. Convolutional Neural Networks
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 6 Weights & Biases at OpenAI
Weights & Biases at OpenAI
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 7 Why Experiment Tracking is Crucial to OpenAI
Why Experiment Tracking is Crucial to OpenAI
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 8 4. Autoencoders
4. Autoencoders
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 9 5. Sentiment Analysis
5. Sentiment Analysis
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 10 6. Recurrent Neural Networks [RNNs]
6. Recurrent Neural Networks [RNNs]
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 11 7. Text Generation using LSTMs and GRUs
7. Text Generation using LSTMs and GRUs
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 12 8. Text Classification Using Convolutional Neural Networks
8. Text Classification Using Convolutional Neural Networks
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 13 9. Hybrid LSTMs [Long Short-Term Memory]
9. Hybrid LSTMs [Long Short-Term Memory]
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 14 Toyota Research Institute on Experiment Tracking with Weights & Biases
Toyota Research Institute on Experiment Tracking with Weights & Biases
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 15 Weights and Biases - Developer Tools for Deep Learning
Weights and Biases - Developer Tools for Deep Learning
Weights & Biases
 16 Introducing Weights & Biases
Introducing Weights & Biases
Weights & Biases
 17 10. Seq2Seq Models
10. Seq2Seq Models
Weights & Biases
 18 11. Transfer Learning for Domain-Specific Image Classification with Small Datasets
11. Transfer Learning for Domain-Specific Image Classification with Small Datasets
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 19 12. One-shot learning for teaching neural networks to classify objects never seen before
12. One-shot learning for teaching neural networks to classify objects never seen before
Weights & Biases
 20 13. Speech Recognition with Convolutional Neural Networks in Keras/TensorFlow
13. Speech Recognition with Convolutional Neural Networks in Keras/TensorFlow
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 21 14. Data Augmentation | Keras
14. Data Augmentation | Keras
Weights & Biases
 22 15. Batch Size and Learning Rate in CNNs
15. Batch Size and Learning Rate in CNNs
Weights & Biases
 23 Applied Deep Learning Fellowship Overview and Project Selection with Josh Tobin (2019)
Applied Deep Learning Fellowship Overview and Project Selection with Josh Tobin (2019)
Weights & Biases
 24 Grading Rubric for AI Applications with Sergey Karayev (2019)
Grading Rubric for AI Applications with Sergey Karayev (2019)
Weights & Biases
 25 16. Video Frame Prediction using CNNs and LSTMs (2019)
16. Video Frame Prediction using CNNs and LSTMs (2019)
Weights & Biases
 26 Image to LaTeX - Applied Deep Learning Fellowship (2019)
Image to LaTeX - Applied Deep Learning Fellowship (2019)
Weights & Biases
 27 17. Build and Deploy an Emotion Classifier (2019)
17. Build and Deploy an Emotion Classifier (2019)
Weights & Biases
 28 Applied Deep Learning - Data Management with Josh Tobin (2019)
Applied Deep Learning - Data Management with Josh Tobin (2019)
Weights & Biases
 29 Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)
Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)
Weights & Biases
 30 Applied Deep Learning - Troubleshooting and Debugging with Josh Tobin (2019)
Applied Deep Learning - Troubleshooting and Debugging with Josh Tobin (2019)
Weights & Biases
 31 Troubleshooting and Iterating ML Models with Lee Redden (2019)
Troubleshooting and Iterating ML Models with Lee Redden (2019)
Weights & Biases
 32 Designing a Machine Learning Project with Neal Khosla (2019)
Designing a Machine Learning Project with Neal Khosla (2019)
Weights & Biases
 33 Lukas Beiwald on ML Tools and Experiment Management (2019)
Lukas Beiwald on ML Tools and Experiment Management (2019)
Weights & Biases
 34 Building Machine Learning Teams with Josh Tobin (2019)
Building Machine Learning Teams with Josh Tobin (2019)
Weights & Biases
 35 Pieter Abeel on Potential Deep Learning Research Directions (2019)
Pieter Abeel on Potential Deep Learning Research Directions (2019)
Weights & Biases
 36 Testing and Deployment of Deep Learning Models with Josh Tobin (2019)
Testing and Deployment of Deep Learning Models with Josh Tobin (2019)
Weights & Biases
 37 Five Lessons for Team-Oriented Research with Peter Welder (2019)
Five Lessons for Team-Oriented Research with Peter Welder (2019)
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 38 Applied Deep Learning - Rosanne Liu on AI Research (2019)
Applied Deep Learning - Rosanne Liu on AI Research (2019)
Weights & Biases
 39 Making the Mid-career Leap from Urban Design to Deep Learning/Data Science
Making the Mid-career Leap from Urban Design to Deep Learning/Data Science
Weights & Biases
 40 Organizing ML projects — W&B walkthrough (2020)
Organizing ML projects — W&B walkthrough (2020)
Weights & Biases
 41 Brandon Rohrer — Machine Learning in Production for Robots
Brandon Rohrer — Machine Learning in Production for Robots
Weights & Biases
 42 Nicolas Koumchatzky — Machine Learning in Production for Self-Driving Cars
Nicolas Koumchatzky — Machine Learning in Production for Self-Driving Cars
Weights & Biases
 43 My experiments with Reinforcement Learning with Jariullah Safi
My experiments with Reinforcement Learning with Jariullah Safi
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 44 Applications of Machine Learning to COVID-19 Research with Isaac Godfried
Applications of Machine Learning to COVID-19 Research with Isaac Godfried
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 45 Testing Machine Learning Models with Eric Schles
Testing Machine Learning Models with Eric Schles
Weights & Biases
 46 How Linear Algebra is not like Algebra with Charles Frye
How Linear Algebra is not like Algebra with Charles Frye
Weights & Biases
 47 Predicting Protein Structures using Deep Learning with Jonathan King
Predicting Protein Structures using Deep Learning with Jonathan King
Weights & Biases
 48 Rachael Tatman — Conversational AI and Linguistics
Rachael Tatman — Conversational AI and Linguistics
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 49 Reformer by Han Lee
Reformer by Han Lee
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 50 Sequence Models with Pujaa Rajan
Sequence Models with Pujaa Rajan
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 51 GitHub Actions & Machine Learning Workflows with Hamel Husain
GitHub Actions & Machine Learning Workflows with Hamel Husain
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 52 Look Mom, No Indices! Vector Calculus with the Fréchet Derivative by Charles Frye
Look Mom, No Indices! Vector Calculus with the Fréchet Derivative by Charles Frye
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 53 Jack Clark — Building Trustworthy AI Systems
Jack Clark — Building Trustworthy AI Systems
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 54 Surprising Utility of Surprise: Why ML Uses Negative Log Probabilities - Charles Frye
Surprising Utility of Surprise: Why ML Uses Negative Log Probabilities - Charles Frye
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 55 Track your machine learning experiments locally, with W&B Local - Chris Van Pelt
Track your machine learning experiments locally, with W&B Local - Chris Van Pelt
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 56 Antipatterns in open source research code with Jariullah Safi
Antipatterns in open source research code with Jariullah Safi
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 57 Attention for time series forecasting & COVID predictions - Isaac Godfried
Attention for time series forecasting & COVID predictions - Isaac Godfried
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 58 Made with ML - Goku Mohandas
Made with ML - Goku Mohandas
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 59 Angela & Danielle — Designing ML Models for Millions of Consumer Robots
Angela & Danielle — Designing ML Models for Millions of Consumer Robots
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 60 Deep Learning Salon by Weights & Biases
Deep Learning Salon by Weights & Biases
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Chapters (17)

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
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
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