Learnings from the DataKind DataDive
More robust web scrapers and using R in Jupyter notebooks.
Stuff I've Learned (by Tags)
R
aws
Last Year’s Learning
Infrastructure, teaching, and preparation for more learning.
AWS Certified Machine Learning Specialty
Notes from my exam prep.
bayesian
Bayes Basics
Intuition, MCMC, and modeling in Stan.
The Connection Between MLE and MAP
MLE as a generalization of MAP with a uniform prior.
causal inference
Last Year’s Learning
Infrastructure, teaching, and preparation for more learning.
computer science
Last Year’s Learning
Infrastructure, teaching, and preparation for more learning.
Debugging
The 9 Indispensable Rules for Finding Even the Most Elusive Software and Hardware Problems.
Understanding Recursion
The secret was the call stack!
dashboard
Business Intelligence Tools
Tableau and Power BI notes, with examples.
Building an Interactive Dashboard in Python
Building a dashboard with Plotly Dash for my NLP with Deep Learning final project.
deep learning
Are You A Cat?
An MLOps Project for the ZenML Month of MLOps Competition.
Sagemaker Batch Transform
Productionizing a fun new model.
Improving Neural Networks
Addressing bias and variance in deep learning models.
Activation Functions for Neural Networks
Why everyone loves ReLU so much.
Why we need GPUs for Deep Learning
A little bit on CPU vs. GPU, and why GPUs excel for deep learning tasks.
distributed computing
The Hadoop Ecosystem
A little bit about a lot of Big Data concepts.
iml
Shapley values: From Game Theory to IML
Some background on Shapley and SHAP implementation.
machine learning
ML Ops Specialization
First course notes, with a focus on improving human-level performance.
Sequenced Regression and Airflow XComs
Practicing some new modeling techniques and Airflow things.
Mathematics for Machine Learning: PCA
The math behind this dimensionality reduction technique.
Mathematics for Machine Learning: Calculus
Fundamental concepts for understanding ML applications.
Mathematics for Machine Learning: Linear Algebra
Fundamental building blocks and applications.
Multicollinearity
Why statisticians care about it and ML folks don’t (but maybe should).
Distance Measures for ML
Some common distance measures and when to use them.
mathematics
Mathematics for Machine Learning: PCA
The math behind this dimensionality reduction technique.
Mathematics for Machine Learning: Calculus
Fundamental concepts for understanding ML applications.
Mathematics for Machine Learning: Linear Algebra
Fundamental building blocks and applications.
mlops
Last Year’s Learning
Infrastructure, teaching, and preparation for more learning.
AWS Certified Machine Learning Specialty
Notes from my exam prep.
Are You A Cat?
An MLOps Project for the ZenML Month of MLOps Competition.
Designing Data-Intensive Applications
My notes from a very good, very long book.
ML Ops Specialization
First course notes, with a focus on improving human-level performance.
Sequenced Regression and Airflow XComs
Practicing some new modeling techniques and Airflow things.
Sagemaker Batch Transform
Productionizing a fun new model.
probability
Common Probability Distributions
A conceptual look at 15 common probability distributions.
Bayes Basics
Intuition, MCMC, and modeling in Stan.
The Connection Between MLE and MAP
MLE as a generalization of MAP with a uniform prior.
python
Learnings from the DataKind DataDive
More robust web scrapers and using R in Jupyter notebooks.
Profiling Python Code
Fixing slow code.
Python Generators
Paths to faster code.
Understanding Recursion
The secret was the call stack!
Shapley values: From Game Theory to IML
Some background on Shapley and SHAP implementation.
Building an Interactive Dashboard in Python
Building a dashboard with Plotly Dash for my NLP with Deep Learning final project.
sql
Recursion in SQL
Implementing recursive queries in SQL.
SQL Injections
Reviewing some prior learnings.
Some Fancier SQL Stuff
Parititioning, indexing, transactions, window functions, UDFs, and pivoting.
Temporal Tables in MariaDB
A nifty way to easily handle system-versioned tables.
A Week of SQL
Refreshing the basics, learning new things, and building a simple app with NodeJS.
statistics
Coefficient Magnitude and Feature Importance
Don’t forget to standardize.
Coarsened Exact Matching
Back to my causal inference roots.
Back to Basics: Interpreting Logistic Regression
Fun with odds, odds-ratios, and probabilities.
Bayes Basics
Intuition, MCMC, and modeling in Stan.
Multicollinearity
Why statisticians care about it and ML folks don’t (but maybe should).
Regression Splines
The intuition behind splines for non-linear data.
The Connection Between MLE and MAP
MLE as a generalization of MAP with a uniform prior.
text data
Shapley values: From Game Theory to IML
Some background on Shapley and SHAP implementation.
Building an Interactive Dashboard in Python
Building a dashboard with Plotly Dash for my NLP with Deep Learning final project.
visualization
Building an Interactive Dashboard in Python
Building a dashboard with Plotly Dash for my NLP with Deep Learning final project.
web scraping
Learnings from the DataKind DataDive
More robust web scrapers and using R in Jupyter notebooks.
Building an Interactive Dashboard in Python
Building a dashboard with Plotly Dash for my NLP with Deep Learning final project.