Sean Taylor — Business Decision Problems
Sean joins us to chat about ML models and tools at Lyft Rideshare Labs, Python vs R, time series forecasting with Prophet, and election forecasting.
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Sean Taylor is a Data Scientist at (and former Head of) Lyft Rideshare Labs, and specializes in methods for solving causal inference and business decision problems. Previously, he was a Research Scientist on Facebook's Core Data Science team. His interests include experiments, causal inference, statistics, machine learning, and economics.
Connect with Sean:
Personal website: https://seanjtaylor.com/
Twitter: https://twitter.com/seanjtaylor
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Chapters (9)
Sneak peek, intro
0:50
Pricing algorithms at Lyft
7:46
Loss functions and ETAs at Lyft
12:59
Models and tools at Lyft
20:46
Python vs R
25:30
Forecasting time series data with Prophet
33:06
Election forecasting and prediction markets
40:55
Comparing and evaluating models
43:22
Bottlenecks in going from research to production
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0. What is machine learning?
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1. Build Your First Machine Learning Model
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Intro to ML: Course Overview
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2. Multi-Layer Perceptrons
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3. Convolutional Neural Networks
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Why Experiment Tracking is Crucial to OpenAI
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4. Autoencoders
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5. Sentiment Analysis
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6. Recurrent Neural Networks [RNNs]
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7. Text Generation using LSTMs and GRUs
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8. Text Classification Using Convolutional Neural Networks
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9. Hybrid LSTMs [Long Short-Term Memory]
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Toyota Research Institute on Experiment Tracking with Weights & Biases
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Introducing Weights & Biases
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11. Transfer Learning for Domain-Specific Image Classification with Small Datasets
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13. Speech Recognition with Convolutional Neural Networks in Keras/TensorFlow
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14. Data Augmentation | Keras
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Pieter Abeel on Potential Deep Learning Research Directions (2019)
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Applied Deep Learning - Rosanne Liu on AI Research (2019)
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My experiments with Reinforcement Learning with Jariullah Safi
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How Linear Algebra is not like Algebra with Charles Frye
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Rachael Tatman — Conversational AI and Linguistics
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Reformer by Han Lee
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Angela & Danielle — Designing ML Models for Millions of Consumer Robots
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