Eric Ma

Network-Analysis-Made-Simple. An introduction to network analysis and applied graph theory using Python and NetworkX

bayesian-stats-modelling-tutorial. How to do Bayesian statistical modelling using numpy and PyMC3

bayesian-analysis-recipes. A collection of Bayesian data analysis recipes using PyMC3

nxviz. Visualization Package for NetworkX

essays-on-data-science. In which I put together my thoughts on the practice of data science.

dl-workshop. Crash course to master gradient-based machine learning. Also secretly a JAX course in disguise!

llamabot. Pythonic class-based interface to LLMs

bayesian-deep-learning-demystified. In which I try to demystify the fundamental concepts behind Bayesian deep learning.

data-testing-tutorial. A short tutorial for data scientists on how to write tests for code + data.

hiveplot. Hive Plots in using Python & matplotlib!

bayesian-stats-talk. Doing Bayesian statistics in Python!

protein-interaction-network. Computes a molecular graph for protein structures.

pyds-cli. Helping you manage your data science projects sanely.

causality. In which I play with the ideas surrounding causality

minimal-flask-example. The simplest complex example that I can think of to show main Flask app concepts.

flu-sequence-predictor. An experimental deep learning & genotype network-based system for predicting new influenza protein sequences.

minimal-streamlit-example. A minimal example of how to use streamlit on Heroku

building-with-llms-made-simple. A tutorial on how to build stuff with large language models, made simple

distributions. Central repository for my distributions figures

score-models. In which I learn about score functions and how they can be used to generate data.

conda-envs. My conda environment YAML files

fundl. A pedagogical, functional-oriented deep learning library built on top of jax.

Circos. Jupyter Notebook

website. Eric Ma's Personal Website

minimal-panel-app. A pedagogical implementation of panel apps served up on a remote machine.

scikit-learn-tutorial. Jupyter Notebook

what-are-probability-distributions. PyCon 2020 Talk on "what probability distributions are"

bayesian-generalized-abcde-testing. PyCon 2019 talk on Bayesian multi-group testing.

resume. Building a resume using nothing but YAML files and Python. A prototype.

pyflatten. A utility for flattening nested data structures into an array.

principled-ds-workflow. Delivered at PyData Boston on 21 July 2020

graph-deep-learning-demystified. An attempt at demystifying graph deep learning

ericmjl.github.io. For my actual source repo, please go to https://github.com/ericmjl/website

probability-distributions-with-python. A talk on what probability distributions are, using Python

graph-fingerprint. A package for using convolutional neural nets to learn a graph fingerprint.

pixi-cuda-environment. Test repo for building Docker container that contains CUDA stuff inside it.

dotfiles. my dotfiles

testing-for-data-scientists. Slides for my talk on testing for data scientists.

pycodestyle-tutorial. Get familiar with Python coding styles, idioms, and get set up to do automatic linting!

awesome-jax. JAX - A curated list of resources https://github.com/google/jax

foam. A personal knowledge management and sharing system for VSCode

probabilistic-programming-tutorial.

bibles. Machine-readable versions of popular English translations of the Bible

autograd-cupy. Autograd wrapper for CuPy

target-prediction. In which I try to replicate the main findings of Ferrero, E., Dunham, I., & Sanseau, P. (2017), Journal of Translational Medicine, 15(1), 182.

normalizing-flows. Deeply learning about normalizing flows.

api-key-precommit. Pre-commit hook to catch that API keys are not accidentally committed

best-resume-ever. :necktie: :briefcase: Build fast :rocket: and easy multiple beautiful resumes and create your best CV ever! Made with Vue and LESS.

software-testing-open-source-and-data-science. Software Testing in Open Source and Data Science: A talk delivered at the Data Umbrella speaker series

questions-for-employers.