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Oct 2, 2017 · post

Probabilistic programming: an annotated bibliography

Earlier this year we launched a research report on probabilistic programming, an emerging programming paradigm that makes it easier to describe and train probabilistic models. The Bayesian probabilistic approach to model building and inference has many advantages in practical data science, including the ability to quantify risk (a superpower in industries like finance and insurance) and the ability to insert institutional knowledge (which is particularly useful when data is scarce). The rise of probabilistic programming languages has made it a more practical technique for time-constrained working data scientists.

If that sounds great to you, and you’re looking to learn more, the first thing you can do is — work with us! We’ll be glad to discuss our report, relevant use cases in your industry, and next steps to incorporate this approach into your data science work.

But you might also enjoy this list of our favorite resources for learning how to do Bayesian inference and build probabilistic programming systems. These are the books, papers and tutorials we found most useful when conducting our research.

Practical books

If you’re just starting out then we recommend either Doing Bayesian Data Analysis by John Kruschke, or Probabilistic Programming and Bayesian Methods for Hackers by Cameron Davidson-Pilon.

Krushke’s book uses R and Stan (and a language called JAGS, that is really only used for teaching these days). Davidson-Pilon uses Python and PyMC. Choose between these books based on your language preferences. If you don’t have a language preference, we at Fast Forward Labs recommend Davidson-Pilon’s book, which is available online, and in particular the PyMC3 edition (there are some important differences between PyMC3 and previous versions).

Theoretical books

The practical books above cover the basics of the theoretical and mathematical side, but if you’d like a deeper dive into why we do what we do, we recommend Data Analysis: A Bayesian Tutorial by Sivia and Skilling. It’s a relatively short and extremely clear book. For an even shorter introduction, we love Brendon Brewer’s lecture notes for STATS 331.

If your background is in economics or life sciences, you may prefer Data Analysis Using Regression and Multilevel/Hierarchical Models by Gelman and Hill. If your background is in physics or engineering, you may prefer Principals of Data Analysis by Prasenjit Saha (which is available free online).

Research

If you’d like a reading list of research papers, there is no better place to start than the excellent annotated bibliography published last year by Alexander Etz and colleagues. Their notes place the research in a historical and conceptual context, so this is in a sense the least technical document in this list. But the papers they discuss are academic research, so you’ll be grappling with some big ideas, including our favorite

The probability that a person is dead (i.e., data) given that a shark has bitten the person’s head off (i.e., theory) is 1. However, given that a person is dead, the probability that a shark has bitten this person’s head off is very close to zero”.

If you’re interested in the algorithmic and computational cutting edge (Hamiltonian Monte Carlo, variational methods, etc.) then we have a blog post that links to a selection of important papers.

Tutorials and articles

Finally, here are a selection of shorter and/or use-case specific practical articles we’ve found interesting and useful:

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Latest posts

Jun 22, 2020 · post

How to Explain HuggingFace BERT for Question Answering NLP Models with TF 2.0

by Victor · Given a question and a passage, the task of Question Answering (QA) focuses on identifying the exact span within the passage that answers the question. Figure 1: In this sample, a BERTbase model gets the answer correct (Achaemenid Persia). Model gradients show that the token “subordinate ..” is impactful in the selection of an answer to the question “Macedonia was under the rule of which country?". This makes sense .. good for BERTbase.
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Jun 16, 2020 · notebook

Evaluating QA: Metrics, Predictions, and the Null Response →

by Melanie · A deep dive into computing QA predictions and when to tell BERT to zip it! In our last post, Building a QA System with BERT on Wikipedia, we used the HuggingFace framework to train BERT on the SQuAD2.0 dataset and built a simple QA system on top of the Wikipedia search engine. This time, we’ll look at how to assess the quality of a BERT-like model for Question Answering.
qa.fastforwardlabs.com
May 19, 2020 · notebook

Building a QA System with BERT on Wikipedia →

by Melanie · So you’ve decided to build a QA system. You want to start with something simple and general so you plan to make it open domain using Wikipedia as a corpus for answering questions. You want to use the best NLP that your compute resources allow (you’re lucky enough to have access to a GPU) so you’re going to focus on the big, flashy Transformer models that are all the rage these days.
qa.fastforwardlabs.com
Apr 28, 2020 · notebook

Intro to Automated Question Answering →

by Melanie · Welcome to the first edition of the Cloudera Fast Forward blog on Natural Language Processing for Question Answering! Throughout this series, we’ll build a Question Answering (QA) system with off-the-shelf algorithms and libraries and blog about our process and what we find along the way. We hope to wind up with a beginning-to-end documentary that provides:
qa.fastforwardlabs.com
Apr 1, 2020 · newsletter

Enterprise Grade ML

by Shioulin · At Cloudera Fast Forward, one of the mechanisms we use to tightly couple machine learning research with application is through application development projects for both internal and external clients. The problems we tackle in these projects are wide ranging and cut across various industries; the end goal is a production system that translates data into business impact. What is Enterprise Grade Machine Learning? Enterprise grade ML, a term mentioned in a paper put forth by Microsoft, refers to ML applications where there is a high level of scrutiny for data handling, model fairness, user privacy, and debuggability.
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Apr 1, 2020 · post

Bias in Knowledge Graphs - Part 1

by Keita · Introduction This is the first part of a series to review Bias in Knowledge Graphs (KG). We aim to describe methods of identifying bias, measuring its impact, and mitigating that impact. For this part, we’ll give a broad overview of this topic. image credit: Mediamodifier from Pixabay Motivation Knowledge graphs, graphs with built-in ontologies, create unique opportunities for data analytics, machine learning, and data mining. They do this by enhancing data with the power of connections and human knowledge.
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Reports

In-depth guides to specific machine learning capabilities

Prototypes

Machine learning prototypes and interactive notebooks
Library

NeuralQA

A usable library for question answering on large datasets.
https://neuralqa.fastforwardlabs.com
Notebook

Explain BERT for Question Answering Models

Tensorflow 2.0 notebook to explain and visualize a HuggingFace BERT for Question Answering model.
https://colab.research.google.com/drive/1tTiOgJ7xvy3sjfiFC9OozbjAX1ho8WN9?usp=sharing
Notebooks

NLP for Question Answering

Ongoing posts and code documenting the process of building a question answering model.
https://qa.fastforwardlabs.com
Notebook

Interpretability Revisited: SHAP and LIME

Explore how to use LIME and SHAP for interpretability.
https://colab.research.google.com/drive/1pjPzsw_uZew-Zcz646JTkRDhF2GkPk0N

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Cloudera Fast Forward is an applied machine learning reseach group.
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