Brandon Rohrer โ Machine Learning in Production for Robots
๐จ๐ปโ๐ปToday our guest is Brandon Rohrer.
Brandon Rohrer is a Mechanical Engineer turned Data Scientist. He's currently a Principal Data Scientist at iRobot and has an incredibly popular Machine Learning course at e2eML where he's made some wildly popular videos on convolutional neural networks and deep learning. His fascination with robots began after watching Luke Skywalker's prosthetic hand in the Empire Strikes Back. He turned this fascination into a PhD from MIT and subsequently found his way to building some incredible data science products at Facebook, Microsoft and now at iRobot.
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Chapters (13)
intro
0:41
Brandon Rohrer Bio
1:31
Challenges faced at iRobot and robotics in general
5:18
Simplicity and performance
9:14
going from mechanical engineering to Machine learning
12:29
what makes general purpose robots hard?
14:02
why is robotics so particularly hard?
15:08
Simulating data
16:50
Toughest part of going from a model on your laptop to production
21:26
What's so great about robots?
28:37
What is an underrated aspect of Machine Learning?
29:43
What is the biggest challenge of machine learning in the real world?
32:01
Where can people find you?
Playlist
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0. What is machine learning?
Weights & Biases
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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VDLS Lavanya Product Walkthrough
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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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DeepCamp AI