Variational Inference: Simple Example (+ Python Demo)
15.7 هزار بار بازدید -
2 سال پیش
-
Variational Bayesian Methods can be
Variational Bayesian Methods can be difficult to understand. In this video, we will look at the simple Exponential-Normal model for which the posterior is intractable. We will show why and then propose a surrogate and perform VI. Here are the notes: https://github.com/Ceyron/machine-lea...
Find the visualization here: https://share.streamlit.io/ceyron/mac...
Variational Inference is a powerful technique in Machine Learning that is used to find approximate posteriors for generative models. In particular, it is being used extensively for Variational Autoencoders.
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📝 : Check out the GitHub Repository of the channel, where I upload all the handwritten notes and source-code files (contributions are very welcome): https://github.com/Ceyron/machine-lea...
📢 : Follow me on LinkedIn or Twitter for updates on the channel and other cool Machine Learning & Simulation stuff: LinkedIn: felix-koehler and Twitter: felix_m_koehler
💸 : If you want to support my work on the channel, you can become a Patreon here: Patreon: MLsim
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⚙️ My Gear:
(Below are affiliate links to Amazon. If you decide to purchase the product or something else on Amazon through this link, I earn a small commission.)
- 🎙️ Microphone: Blue Yeti: https://amzn.to/3NU7OAs
- ⌨️ Logitech TKL Mechanical Keyboard: https://amzn.to/3JhEtwp
- 🎨 Gaomon Drawing Tablet (similar to a WACOM Tablet, but cheaper, works flawlessly under Linux): https://amzn.to/37katmf
- 🔌 Laptop Charger: https://amzn.to/3ja0imP
- 💻 My Laptop (generally I like the Dell XPS series): https://amzn.to/38xrABL
- 📱 My Phone: Fairphone 4 (I love the sustainability and repairability aspect of it): https://amzn.to/3Jr4ZmV
If I had to purchase these items again, I would probably change the following:
- 🎙️ Rode NT: https://amzn.to/3NUIGtw
- 💻 Framework Laptop (I do not get a commission here, but I love the vision of Framework. It will definitely be my next Ultrabook): https://frame.work
As an Amazon Associate I earn from qualifying purchases.
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Timestamps:
00:00 Introduction
00:38 Agenda
01:30 Joint distribution
04:49 Trying to find the true posterior (and fail)
14:45 Visualization (Joint, Posterior & Surrogate)
19:05 Recap: Variational Inference & ELBO
21:56 Introducing a parametric surrogate posterior
24:45 Remark: Approximating the ELBO by sampling
27:24 Performing Variational Inference (Optimizing ELBO)
38:38 Python example with TensorFlow Probability
47:09 Outro
Find the visualization here: https://share.streamlit.io/ceyron/mac...
Variational Inference is a powerful technique in Machine Learning that is used to find approximate posteriors for generative models. In particular, it is being used extensively for Variational Autoencoders.
-------
📝 : Check out the GitHub Repository of the channel, where I upload all the handwritten notes and source-code files (contributions are very welcome): https://github.com/Ceyron/machine-lea...
📢 : Follow me on LinkedIn or Twitter for updates on the channel and other cool Machine Learning & Simulation stuff: LinkedIn: felix-koehler and Twitter: felix_m_koehler
💸 : If you want to support my work on the channel, you can become a Patreon here: Patreon: MLsim
-------
⚙️ My Gear:
(Below are affiliate links to Amazon. If you decide to purchase the product or something else on Amazon through this link, I earn a small commission.)
- 🎙️ Microphone: Blue Yeti: https://amzn.to/3NU7OAs
- ⌨️ Logitech TKL Mechanical Keyboard: https://amzn.to/3JhEtwp
- 🎨 Gaomon Drawing Tablet (similar to a WACOM Tablet, but cheaper, works flawlessly under Linux): https://amzn.to/37katmf
- 🔌 Laptop Charger: https://amzn.to/3ja0imP
- 💻 My Laptop (generally I like the Dell XPS series): https://amzn.to/38xrABL
- 📱 My Phone: Fairphone 4 (I love the sustainability and repairability aspect of it): https://amzn.to/3Jr4ZmV
If I had to purchase these items again, I would probably change the following:
- 🎙️ Rode NT: https://amzn.to/3NUIGtw
- 💻 Framework Laptop (I do not get a commission here, but I love the vision of Framework. It will definitely be my next Ultrabook): https://frame.work
As an Amazon Associate I earn from qualifying purchases.
-------
Timestamps:
00:00 Introduction
00:38 Agenda
01:30 Joint distribution
04:49 Trying to find the true posterior (and fail)
14:45 Visualization (Joint, Posterior & Surrogate)
19:05 Recap: Variational Inference & ELBO
21:56 Introducing a parametric surrogate posterior
24:45 Remark: Approximating the ELBO by sampling
27:24 Performing Variational Inference (Optimizing ELBO)
38:38 Python example with TensorFlow Probability
47:09 Outro
2 سال پیش
در تاریخ 1401/02/09 منتشر شده
است.
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