Angela & Danielle โ€” Designing ML Models for Millions of Consumer Robots

Weights & Biases ยท Beginner ยท๐Ÿ“ ML Fundamentals ยท5y ago
๐Ÿ‘ฉโ€๐Ÿ’ป๐Ÿ‘ฉโ€๐Ÿ’ปOn 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 industriesโ€”spanning 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 Hโ€ฆ
Watch on YouTube โ†— (saves to browser)

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

Playlist

Uploads from Weights & Biases ยท Weights & Biases ยท 60 of 60

โ† Previous Next โ†’
1 0. What is machine learning?
0. What is machine learning?
Weights & Biases
 2 1. Build Your First Machine Learning Model
1. Build Your First Machine Learning Model
Weights & Biases
 3 Intro to ML: Course Overview
Intro to ML: Course Overview
Weights & Biases
 4 2. Multi-Layer Perceptrons
2. Multi-Layer Perceptrons
Weights & Biases
 5 3. Convolutional Neural Networks
3. Convolutional Neural Networks
Weights & Biases
 6 Weights & Biases at OpenAI
Weights & Biases at OpenAI
Weights & Biases
 7 Why Experiment Tracking is Crucial to OpenAI
Why Experiment Tracking is Crucial to OpenAI
Weights & Biases
 8 4. Autoencoders
4. Autoencoders
Weights & Biases
 9 5. Sentiment Analysis
5. Sentiment Analysis
Weights & Biases
 10 6. Recurrent Neural Networks [RNNs]
6. Recurrent Neural Networks [RNNs]
Weights & Biases
 11 7. Text Generation using LSTMs and GRUs
7. Text Generation using LSTMs and GRUs
Weights & Biases
 12 8. Text Classification Using Convolutional Neural Networks
8. Text Classification Using Convolutional Neural Networks
Weights & Biases
 13 9. Hybrid LSTMs [Long Short-Term Memory]
9. Hybrid LSTMs [Long Short-Term Memory]
Weights & Biases
 14 Toyota Research Institute on Experiment Tracking with Weights & Biases
Toyota Research Institute on Experiment Tracking with Weights & Biases
Weights & Biases
 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
Weights & Biases
 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
Weights & Biases
 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)
Weights & Biases
 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 โ€” W&B walkthrough (2020)
Organizing ML projects โ€” W&B walkthrough (2020)
Weights & Biases
 41 Brandon Rohrer โ€” Machine Learning in Production for Robots
Brandon Rohrer โ€” Machine Learning in Production for Robots
Weights & Biases
 42 Nicolas Koumchatzky โ€” Machine Learning in Production for Self-Driving Cars
Nicolas Koumchatzky โ€” 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
Weights & Biases
 44 Applications of Machine Learning to COVID-19 Research with Isaac Godfried
Applications of Machine Learning to COVID-19 Research with Isaac Godfried
Weights & Biases
 45 VDLS Lavanya Product Walkthrough
VDLS Lavanya Product Walkthrough
Weights & Biases
 46 Testing Machine Learning Models with Eric Schles
Testing Machine Learning Models with Eric Schles
Weights & Biases
 47 How Linear Algebra is not like Algebra with Charles Frye
How Linear Algebra is not like Algebra with Charles Frye
Weights & Biases
 48 Predicting Protein Structures using Deep Learning with Jonathan King
Predicting Protein Structures using Deep Learning with Jonathan King
Weights & Biases
 49 Rachael Tatman โ€” Conversational AI and Linguistics
Rachael Tatman โ€” Conversational AI and Linguistics
Weights & Biases
 50 Reformer by Han Lee
Reformer by Han Lee
Weights & Biases
 51 Sequence Models with Pujaa Rajan
Sequence Models with Pujaa Rajan
Weights & Biases
 52 GitHub Actions & Machine Learning Workflows with Hamel Husain
GitHub Actions & Machine Learning Workflows with Hamel Husain
Weights & Biases
 53 Look Mom, No Indices! Vector Calculus with the Frรฉchet Derivative by Charles Frye
Look Mom, No Indices! Vector Calculus with the Frรฉchet Derivative by Charles Frye
Weights & Biases
 54 Jack Clark โ€” Building Trustworthy AI Systems
Jack Clark โ€” Building Trustworthy AI Systems
Weights & Biases
 55 Surprising Utility of Surprise: Why ML Uses Negative Log Probabilities - Charles Frye
Surprising Utility of Surprise: Why ML Uses Negative Log Probabilities - Charles Frye
Weights & Biases
 56 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
Weights & Biases
 57 Antipatterns in open source research code with Jariullah Safi
Antipatterns in open source research code with Jariullah Safi
Weights & Biases
 58 Attention for time series forecasting & COVID predictions - Isaac Godfried
Attention for time series forecasting & COVID predictions - Isaac Godfried
Weights & Biases
 59 Made with ML - Goku Mohandas
Made with ML - Goku Mohandas
Weights & Biases
 โ–ถ Angela & Danielle โ€” Designing ML Models for Millions of Consumer Robots
Angela & Danielle โ€” Designing ML Models for Millions of Consumer Robots
Weights & Biases
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