Angela & Danielle โ Designing ML Models for Millions of Consumer Robots
Skills:
ML Maths Basics50%
๐ฉโ๐ป๐ฉโ๐ปOn this episode of Gradient Dissent our guests are Angela Bassa and Danielle Dean!
Angela is an expert in building and leading data teams. An MIT-trained and Edelman-award-winning mathematician, she has over 15 years of experience across industriesโspanning finance, life sciences, agriculture, marketing, energy, software, and robotics. Angela heads Data Science and Machine Learning at iRobot, where her teams help bring intelligence to a global fleet of millions of consumer robots. She is also a renowned keynote speaker and author, with credits including the Wall Street Journal and Harvard Business Review.
Follow Angela on twitter: https://twitter.com/angebassa
And on her website: https://www.angelabassa.com/
Danielle Dean, PhD is the Technical Director of Machine Learning at iRobot where she is helping lead the intelligence revolution for robots. She leads a team that leverages machine learning, reinforcement learning, and software engineering to build algorithms that will result in massive improvements in our robots. Before iRobot, Danielle was a Principal Data Scientist Lead at Microsoft Corp. in AzureCAT Engineering within the Cloud AI Platform division.
Follow Danielle on Twitter: https://twitter.com/danielleodean
Topics covered:
0:00 sneek peek
0:19 intro and bios
0:49 how do you approach building a technical team?
6:40 building an ML skillset at iRobot
12:27 breaking down the teams around research, modeling, and deployment
24:13 lessons learned on getting diverse teams to work together
26:39 balancing company needs vs academic advancements
29:07 hiring at iRobot vs other places
30:30 Quality control and testing to production
35:44 how to deal with non-deterministic models - updating data, reproduciblility
39:20 better modeling vs data, what is most important?
42:15 data augmentation vs model architecture which is more important?
44:07 which deep learning framework do you use?
46:43 the most underrated aspect of ML
48:44 biggest challenge in maki
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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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Weights & Biases at OpenAI
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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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Weights and Biases - Developer Tools for Deep Learning
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Introducing Weights & Biases
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10. Seq2Seq Models
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11. Transfer Learning for Domain-Specific Image Classification with Small Datasets
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12. One-shot learning for teaching neural networks to classify objects never seen before
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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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15. Batch Size and Learning Rate in CNNs
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Applied Deep Learning Fellowship Overview and Project Selection with Josh Tobin (2019)
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Grading Rubric for AI Applications with Sergey Karayev (2019)
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16. Video Frame Prediction using CNNs and LSTMs (2019)
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Image to LaTeX - Applied Deep Learning Fellowship (2019)
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17. Build and Deploy an Emotion Classifier (2019)
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Applied Deep Learning - Data Management with Josh Tobin (2019)
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Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)
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Applied Deep Learning - Troubleshooting and Debugging with Josh Tobin (2019)
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Troubleshooting and Iterating ML Models with Lee Redden (2019)
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Designing a Machine Learning Project with Neal Khosla (2019)
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Lukas Beiwald on ML Tools and Experiment Management (2019)
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Building Machine Learning Teams with Josh Tobin (2019)
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Pieter Abeel on Potential Deep Learning Research Directions (2019)
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Testing and Deployment of Deep Learning Models with Josh Tobin (2019)
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Five Lessons for Team-Oriented Research with Peter Welder (2019)
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Applied Deep Learning - Rosanne Liu on AI Research (2019)
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Making the Mid-career Leap from Urban Design to Deep Learning/Data Science
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Organizing ML projects โ W&B walkthrough (2020)
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Brandon Rohrer โ Machine Learning in Production for Robots
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Nicolas Koumchatzky โ Machine Learning in Production for Self-Driving Cars
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My experiments with Reinforcement Learning with Jariullah Safi
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Applications of Machine Learning to COVID-19 Research with Isaac Godfried
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Testing Machine Learning Models with Eric Schles
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How Linear Algebra is not like Algebra with Charles Frye
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Predicting Protein Structures using Deep Learning with Jonathan King
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Rachael Tatman โ Conversational AI and Linguistics
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Reformer by Han Lee
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Sequence Models with Pujaa Rajan
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GitHub Actions & Machine Learning Workflows with Hamel Husain
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Look Mom, No Indices! Vector Calculus with the Frรฉchet Derivative by Charles Frye
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Jack Clark โ Building Trustworthy AI Systems
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Surprising Utility of Surprise: Why ML Uses Negative Log Probabilities - Charles Frye
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Track your machine learning experiments locally, with W&B Local - Chris Van Pelt
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Antipatterns in open source research code with Jariullah Safi
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Attention for time series forecasting & COVID predictions - Isaac Godfried
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Made with ML - Goku Mohandas
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Angela & Danielle โ Designing ML Models for Millions of Consumer Robots
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Deep Learning Salon by Weights & Biases
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Chapters (15)
sneek peek
0:19
intro and bios
0:49
how do you approach building a technical team?
6:40
building an ML skillset at iRobot
12:27
breaking down the teams around research, modeling, and deployment
24:13
lessons learned on getting diverse teams to work together
26:39
balancing company needs vs academic advancements
29:07
hiring at iRobot vs other places
30:30
Quality control and testing to production
35:44
how to deal with non-deterministic models - updating data, reproduciblility
39:20
better modeling vs data, what is most important?
42:15
data augmentation vs model architecture which is more important?
44:07
which deep learning framework do you use?
46:43
the most underrated aspect of ML
48:44
biggest challenge in maki
๐
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