Top all-time · #7414
while my_mcmc: gently(samples)
Personal blog · twiecki.github.io/blogdataai
while my_mcmc: gently(samples) is a personal blog at twiecki.github.io about data and ai. Since 2013, 11 different Hacker News readers have linked it 23 times, most often “Bayesian Deep Learning”. No feed was found, so read it on the site itself.
Too few sampled comments (3) to say how often HN recommends it.?
Why HN links it
HN readers link Thomas Wiecki's blog for practical Bayesian modelling posts: using ADVI to get uncertainty out of a network, and building Bayesian neural networks with pymc3.
Summarised by Claude from a sample of 3 HN comments that link here. Each comment is marked recommending, critical or neutral (citing without judging); how this works.
Most-linked posts?
- Bayesian Deep Learning3 people · 3 mentions · first linked 2016-06-01
- A modern guide to getting started with data science and Python (2014)2 people · 2 mentions · first linked 2014-11-18
- MCMC sampling for dummies2 people · 2 mentions · first linked 2015-11-10
- Hierarchical Bayesian Neural Networks with Informative Priors2 people · 2 mentions · first linked 2018-08-13
What Hacker News says?
Yarin Gal also wrote the excellent "What My Deep Model Doesn't Know..." [0] in 2015. If these ideas look interesting, you might also want to check out Thomas Wiecki's blog [1] with a practical application of ADVI (a form of the variational inference Yarin discusses) to get uncertainty out of a network. [0] http://mlg.eng.cam.ac.uk/yarin/blog_3d801aa532c1ce.html [1] http://twiecki.github.io/blog/2016/06/01/bayesian-d…
Comment linking /blog/2016/06/01/bayesian-deep-learning/
on “Uncertainty in Deep Learning (2016)”Submitted Bayesian Deep Learning as a story · 7 points
HN discussionI don't think it has to be either or. For example, you can implement a Deep Net in a probabilistic programming framework ( http://twiecki.github.io/blog/2016/07/05/bayesian-deep-learn... ) like PyMC3. The inference here is not using sampling but rather ADVI ( http://pymc-devs.github.io/pymc3/api.html#advi ) which is almost as general but much much faster, and can be run on sub-samples of the data (similar to stochas…
Comment linking /blog/2016/07/05/bayesian-deep-learning/
on “Probabilistic Programming”Submitted The Best Of Both Worlds: Hierarchical Linear Regression in PyMC3 as a story · 4 points
HN discussionPyMC3 ( http://pymc-devs.github.io/pymc3/ ) has all the powerful samplers that Stan has (i.e. NUTS) as well as support for discrete priors. It allows you to specify your models in pure Python, supports matrix algebra, and also has variational inference which allows for large-ish scale machine learning (e.g. here is a blog post where I train a Bayesian Neural Net on MNIST http://twiecki.github.io/blog/2016/07/05/baye…
Comment linking /blog/2016/07/05/bayesian-deep-learning/
on “Probabilistic Programming”Submitted Bayesian Deep Learning – Neural Networks in PyMC3 with Variational Inference as a story · 3 points
HN discussionI don't have any Google Hangout chat messages to run the first example of using jupyter. I know that you are not going to share your data, but it should be handy if some fake conversations could be included. People like me like to first install the applications and then run it to see whether it works as claimed. I installed the conda distribution and the jupyter notebook works correctly. (I installed conda in ubuntu…
Comment linking /
on “A Python tutorial on Bayesian modeling techniques”Submitted Hierarchical Bayesian Neural Networks with Informative Priors as a story · 2 points
HN discussionA few comments have mentioned neural nets in this post. adamnemecek mentions in this thread that PGMs are a superset of neural networks, and and Thomas Wiecki has a few excellent blog posts on creating bayesian neural networks using pymc3.[0][1][2] If you're curious about how these two concepts can be brought together I highly recommend reading through these three posts. [0] http://twiecki.github.io/blog/2016/06/01/…
Comment linking /blog/2017/03/14/random-walk-deep-net/
on “An Introduction to Probabilistic Graphical Models (2003) [pdf]”Submitted An intuitive, visual guide to copulas as a story · 2 points
HN discussionSubmitted Bayesian Deep Learning – Neural Networks Estimated with Variational Inference as a story · 2 points
HN discussionSubmitted MCMC sampling for dummies as a story · 2 points
HN discussionSubmitted A modern guide to getting started with data science and Python (2014) as a story · 2 points
HN discussionSubmitted Probabilistic Programming in Python as a story · 2 points
HN discussionSubmitted Hierarchical Bayesian Neural Networks with Informative Priors as a story · 1 points
HN discussionSubmitted MCMC sampling for dummies as a story · 1 points
HN discussionSubmitted Probabilistic Programming in Quant Finance using PyMC3 as a story · 1 points
HN discussionSubmitted A modern guide to getting started with Data Science and Python as a story · 1 points
HN discussionSubmitted Easily distributing a parallel IPython Notebook on a cluster as a story · 1 points
HN discussionSubmitted Visualizing MCMC with PyMC and Matplotlib as a story · 1 points
HN discussionSubmitted The Inference Button: Bayesian GLMs made easy with PyMC3 as a story · 1 points
HN discussion