Josh Bloom — The Link Between Astronomy and ML

Weights & Biases · Beginner ·📐 ML Fundamentals ·4y ago
Josh explains how astronomy and machine learning have informed each other, their current limitations, and where their intersection goes from here. --- Josh is a Professor of Astronomy and Chair of the Astronomy Department at UC Berkeley. His research interests include the intersection of machine learning and physics, time-domain transients events, artificial intelligence, and optical/infared instrumentation. Much of Josh's current group activities can be found at ML4Science. Connect with Josh: 📍 Twitter: https://twitter.com/profjsb 📍 Personal Website: https://joshbloom.org/ --- ⏳ Timestamps: 0:00 Intro, sneak peek 1:15 How astronomy has informed ML 4:20 The big questions in astronomy today 10:15 On dark matter and dark energy 16:37 Finding life on other planets 19:55 Driving advancements in astronomy 27:05 Putting telescopes in space 31:05 Why Josh started using ML in his research 33:54 Crowdsourcing in astronomy 36:20 How ML has (and hasn't) informed astronomy 47:22 The next generation of cross-functional grad students 50:50 How Josh started coding 56:11 Incentives and maintaining research codebases 1:00:01 ML4Science's tech stack 1:02:11 Uncertainty quantification in a sensor-based world 1:04:28 Why it's not good to always get an answer 1:07:47 Outro 🌟 http://wandb.me/gd-josh-bloom 🌟 Links: 1. Big Rip, the hypothesis that the universe will eventually rip itself apart - https://en.wikipedia.org/wiki/Big_Rip 2. TESS satellite, a space telescope designed to look for exoplanets - https://www.nasa.gov/content/about-tess/ 3. ML4Science, Josh's research group - https://www.ml4science.org/home 4. Galaxy Zoo, a crowdsourcing project to classify galaxies - https://www.zooniverse.org/projects/zookeeper/galaxy-zoo/ 5. Pea galaxy, a type of galaxy first dsicovered in 2007 as part of Galaxy Zoo - https://en.wikipedia.org/wiki/Pea_galaxy 6. "Searching for exotic particles in high-energy physics with deep learning" (Baldi et al., 2014), using ML to "rediscover" the Hig
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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
2. Multi-Layer Perceptrons
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 5 3. Convolutional Neural Networks
3. Convolutional Neural Networks
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 6 Weights & Biases at OpenAI
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
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 15 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)
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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)
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 26 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)
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 29 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)
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 31 Troubleshooting and Iterating ML Models with Lee Redden (2019)
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 32 Designing a Machine Learning Project with Neal Khosla (2019)
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 33 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)
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 36 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
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 45 Testing Machine Learning Models with Eric Schles
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 46 How Linear Algebra is not like Algebra with Charles Frye
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 47 Predicting Protein Structures using Deep Learning with Jonathan King
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 48 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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 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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 59 Angela & Danielle — Designing ML Models for Millions of Consumer Robots
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Chapters (17)

Intro, sneak peek
1:15 How astronomy has informed ML
4:20 The big questions in astronomy today
10:15 On dark matter and dark energy
16:37 Finding life on other planets
19:55 Driving advancements in astronomy
27:05 Putting telescopes in space
31:05 Why Josh started using ML in his research
33:54 Crowdsourcing in astronomy
36:20 How ML has (and hasn't) informed astronomy
47:22 The next generation of cross-functional grad students
50:50 How Josh started coding
56:11 Incentives and maintaining research codebases
1:00:01 ML4Science's tech stack
1:02:11 Uncertainty quantification in a sensor-based world
1:04:28 Why it's not good to always get an answer
1:07:47 Outro
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