Josh Tobin — Productionizing ML Models
Josh Tobin is a researcher working at the intersection of machine learning and robotics. His research focuses on applying deep reinforcement learning, generative models, and synthetic data to problems in robotic perception and control.
Additionally, he co-organizes a machine learning training program for engineers to learn about production-ready deep learning called Full Stack Deep Learning. https://fullstackdeeplearning.com/
Josh did his PhD in Computer Science at UC Berkeley advised by Pieter Abbeel and was a research scientist at OpenAI for 3 years during his PhD.
Finally, Josh created t…
Watch on YouTube ↗
(saves to browser)
Chapters (13)
introduction
1:21
the origin of full stack deep learning
5:31
how did you get up to speed in Deep learning?
6:45
how McKinsey influence Josh's work
8:18
Slow down and change one thing at a time
12:17
how do you know your models are working?
20:25
Weights and Biases ad
22:03
how to learn and be successful at ML
25:57
Sim2Real
35:50
Rubix cube robot hand behind-the-scenes
41:34
moving on from OpenAI, incremental improvements
43:08
the dirty secret of machine learning
47:30
Most underrated topic of ML
Playlist
Uploads from Weights & Biases · Weights & Biases · 0 of 60
← Previous
Next →
1
2
3
4
5
6
7
8
9
10
![6. Recurrent Neural Networks [RNNs]](https://i.ytimg.com/vi/8lbGjKhrJOo/mqdefault.jpg)
11
12
13
![9. Hybrid LSTMs [Long Short-Term Memory]](https://i.ytimg.com/vi/NysY9FN9Uac/mqdefault.jpg)
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
0. What is machine learning?
Weights & Biases
1. Build Your First Machine Learning Model
Weights & Biases
Intro to ML: Course Overview
Weights & Biases
2. Multi-Layer Perceptrons
Weights & Biases
3. Convolutional Neural Networks
Weights & Biases
Weights & Biases at OpenAI
Weights & Biases
Why Experiment Tracking is Crucial to OpenAI
Weights & Biases
4. Autoencoders
Weights & Biases
5. Sentiment Analysis
Weights & Biases
6. Recurrent Neural Networks [RNNs]
Weights & Biases
7. Text Generation using LSTMs and GRUs
Weights & Biases
8. Text Classification Using Convolutional Neural Networks
Weights & Biases
9. Hybrid LSTMs [Long Short-Term Memory]
Weights & Biases
Toyota Research Institute on Experiment Tracking with Weights & Biases
Weights & Biases
Weights and Biases - Developer Tools for Deep Learning
Weights & Biases
Introducing Weights & Biases
Weights & Biases
10. Seq2Seq Models
Weights & Biases
11. Transfer Learning for Domain-Specific Image Classification with Small Datasets
Weights & Biases
12. One-shot learning for teaching neural networks to classify objects never seen before
Weights & Biases
13. Speech Recognition with Convolutional Neural Networks in Keras/TensorFlow
Weights & Biases
14. Data Augmentation | Keras
Weights & Biases
15. Batch Size and Learning Rate in CNNs
Weights & Biases
Applied Deep Learning Fellowship Overview and Project Selection with Josh Tobin (2019)
Weights & Biases
Grading Rubric for AI Applications with Sergey Karayev (2019)
Weights & Biases
16. Video Frame Prediction using CNNs and LSTMs (2019)
Weights & Biases
Image to LaTeX - Applied Deep Learning Fellowship (2019)
Weights & Biases
17. Build and Deploy an Emotion Classifier (2019)
Weights & Biases
Applied Deep Learning - Data Management with Josh Tobin (2019)
Weights & Biases
Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)
Weights & Biases
Applied Deep Learning - Troubleshooting and Debugging with Josh Tobin (2019)
Weights & Biases
Troubleshooting and Iterating ML Models with Lee Redden (2019)
Weights & Biases
Designing a Machine Learning Project with Neal Khosla (2019)
Weights & Biases
Lukas Beiwald on ML Tools and Experiment Management (2019)
Weights & Biases
Building Machine Learning Teams with Josh Tobin (2019)
Weights & Biases
Pieter Abeel on Potential Deep Learning Research Directions (2019)
Weights & Biases
Testing and Deployment of Deep Learning Models with Josh Tobin (2019)
Weights & Biases
Five Lessons for Team-Oriented Research with Peter Welder (2019)
Weights & Biases
Applied Deep Learning - Rosanne Liu on AI Research (2019)
Weights & Biases
Making the Mid-career Leap from Urban Design to Deep Learning/Data Science
Weights & Biases
Organizing ML projects — W&B walkthrough (2020)
Weights & Biases
Brandon Rohrer — Machine Learning in Production for Robots
Weights & Biases
Nicolas Koumchatzky — Machine Learning in Production for Self-Driving Cars
Weights & Biases
My experiments with Reinforcement Learning with Jariullah Safi
Weights & Biases
Applications of Machine Learning to COVID-19 Research with Isaac Godfried
Weights & Biases
VDLS Lavanya Product Walkthrough
Weights & Biases
Testing Machine Learning Models with Eric Schles
Weights & Biases
How Linear Algebra is not like Algebra with Charles Frye
Weights & Biases
Predicting Protein Structures using Deep Learning with Jonathan King
Weights & Biases
Rachael Tatman — Conversational AI and Linguistics
Weights & Biases
Reformer by Han Lee
Weights & Biases
Sequence Models with Pujaa Rajan
Weights & Biases
GitHub Actions & Machine Learning Workflows with Hamel Husain
Weights & Biases
Look Mom, No Indices! Vector Calculus with the Fréchet Derivative by Charles Frye
Weights & Biases
Jack Clark — Building Trustworthy AI Systems
Weights & Biases
Surprising Utility of Surprise: Why ML Uses Negative Log Probabilities - Charles Frye
Weights & Biases
Track your machine learning experiments locally, with W&B Local - Chris Van Pelt
Weights & Biases
Antipatterns in open source research code with Jariullah Safi
Weights & Biases
Attention for time series forecasting & COVID predictions - Isaac Godfried
Weights & Biases
Made with ML - Goku Mohandas
Weights & Biases
Angela & Danielle — Designing ML Models for Millions of Consumer Robots
Weights & Biases
DeepCamp AI