Peter Welinder — Deep Reinforcement Learning and Robotics

Weights & Biases · Beginner ·📐 ML Fundamentals ·5y ago
Peter Welinder is a research scientist and roboticist at OpenAI. Before that, he was an engineer at Dropbox and ran the machine learning team, and before that, he co-founded Anchovi Labs a startup using Computer Vision to organize photos that was acquired by Dropbox in 2012. In this episode of our podcast, Peter shares his experiences and the challenges associated with building a robotic hand that can solve a rubix cube. The complete show notes (transcript and links) can be found here: http://wandb.me/gd-peter-welinder --- Read some of Peter’s Articles: https://openai.com/blog/authors/peter/ Follow Peter on Twitter: https://twitter.com/npew --- Timestamps: 0:00 Intro 1:01 How Peter got into deep learning 3:50 Working at Dropbox 12:23 Joining OpenAI's robotic team 16:51 Believing in reinforcement learning 24:29 Tackling the robotic hand problem 31:44 Building a research team 38:15 Switching from TensorFlow to PyTorch 41:43 Releasing internal OpenAI tools 44:55 How OpenAI uses Weights & Biases 49:57 Over-confident models and trying simple things 54:04 Outro --- 🎙 Get our podcasts on these platforms: Soundcloud: http://wandb.me/soundcloud Apple Podcasts: http://wandb.me/apple-podcasts Spotify: http://wandb.me/spotify Google: http://wandb.me/gd_google YouTube: http://wandb.me/youtube --- Host: Lukas Biewald - https://twitter.com/l2k Producer: Lavanya Shukla - https://twitter.com/lavanyaai Editor: Cayla Sharp - http://caylasharp.com/
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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
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 5 3. Convolutional Neural Networks
3. Convolutional Neural Networks
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Weights & Biases at OpenAI
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 7 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
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 16 Introducing Weights & Biases
Introducing Weights & Biases
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 17 10. Seq2Seq Models
10. Seq2Seq Models
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 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
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 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
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 22 15. Batch Size and Learning Rate in CNNs
15. Batch Size and Learning Rate in CNNs
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 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)
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 24 Grading Rubric for AI Applications with Sergey Karayev (2019)
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 25 16. Video Frame Prediction using CNNs and LSTMs (2019)
16. Video Frame Prediction using CNNs and LSTMs (2019)
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 26 Image to LaTeX - Applied Deep Learning Fellowship (2019)
Image to LaTeX - Applied Deep Learning Fellowship (2019)
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 27 17. Build and Deploy an Emotion Classifier (2019)
17. Build and Deploy an Emotion Classifier (2019)
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 28 Applied Deep Learning - Data Management with Josh Tobin (2019)
Applied Deep Learning - Data Management with Josh Tobin (2019)
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 29 Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)
Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)
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 30 Applied Deep Learning - Troubleshooting and Debugging with Josh Tobin (2019)
Applied Deep Learning - Troubleshooting and Debugging with Josh Tobin (2019)
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 31 Troubleshooting and Iterating ML Models with Lee Redden (2019)
Troubleshooting and Iterating ML Models with Lee Redden (2019)
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 32 Designing a Machine Learning Project with Neal Khosla (2019)
Designing a Machine Learning Project with Neal Khosla (2019)
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 33 Lukas Beiwald on ML Tools and Experiment Management (2019)
Lukas Beiwald on ML Tools and Experiment Management (2019)
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 34 Building Machine Learning Teams with Josh Tobin (2019)
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 35 Pieter Abeel on Potential Deep Learning Research Directions (2019)
Pieter Abeel on Potential Deep Learning Research Directions (2019)
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 36 Testing and Deployment of Deep Learning Models with Josh Tobin (2019)
Testing and Deployment of Deep Learning Models with Josh Tobin (2019)
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 37 Five Lessons for Team-Oriented Research with Peter Welder (2019)
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 38 Applied Deep Learning - Rosanne Liu on AI Research (2019)
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 39 Making the Mid-career Leap from Urban Design to Deep Learning/Data Science
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 40 Organizing ML projects — W&B walkthrough (2020)
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 41 Brandon Rohrer — Machine Learning in Production for Robots
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 42 Nicolas Koumchatzky — Machine Learning in Production for Self-Driving Cars
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 43 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
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 46 How Linear Algebra is not like Algebra with Charles Frye
How Linear Algebra is not like Algebra with Charles Frye
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 47 Predicting Protein Structures using Deep Learning with Jonathan King
Predicting Protein Structures using Deep Learning with Jonathan King
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 48 Rachael Tatman — Conversational AI and Linguistics
Rachael Tatman — Conversational AI and Linguistics
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 49 Reformer by Han Lee
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 50 Sequence Models with Pujaa Rajan
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 51 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
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 53 Jack Clark — Building Trustworthy AI Systems
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 54 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
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 56 Antipatterns in open source research code with Jariullah Safi
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 57 Attention for time series forecasting & COVID predictions - Isaac Godfried
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 58 Made with ML - Goku Mohandas
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 59 Angela & Danielle — Designing ML Models for Millions of Consumer Robots
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Chapters (12)

Intro
1:01 How Peter got into deep learning
3:50 Working at Dropbox
12:23 Joining OpenAI's robotic team
16:51 Believing in reinforcement learning
24:29 Tackling the robotic hand problem
31:44 Building a research team
38:15 Switching from TensorFlow to PyTorch
41:43 Releasing internal OpenAI tools
44:55 How OpenAI uses Weights & Biases
49:57 Over-confident models and trying simple things
54:04 Outro
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