AI Active Learning Attention Auto Encoders Bayesian Inference Beginner Deep Learning Images Industry Learning to Rank MLB Machine Learning Model Evaluation Model Evaluations Monte Carlo Online Learning Opinion Projections Recommendation Systems Software Time Series Variational Inference World Series docker helm keras kubernetes pandas pymc pymc3 sklearn tensorflow tensorflow2
AI
- Active learning and deep probabilistic ensembles
- Image Search Take 2 - Convolutional Autoencoders
- Image search with autoencoders
Active Learning
Attention
- Deep learning for tabular data 2 - Debunking the myth of the black box
- An Overview of Attention Is All You Need
Auto Encoders
Bayesian Inference
- PyMCon Afterword
- MLB 2020 Postseason Projections
- My Quarantine Playlist
- Predicting Pete Alonso's 2020 Performance
- 2019 World Series Pitcher Matchups
- Active learning and deep probabilistic ensembles
- World Series Projections
- Hierarchical Bayesian Ranking
- Hypothesis Testing For Humans - Do The Umps Really Want to Go Home
- Bayesian Online Learning
- A brief primer on conjugate priors
Beginner
Deep Learning
- Deep Learning for Time Series
- Neural Networks Explained
- Deep learning for tabular data 2 - Debunking the myth of the black box
- Deep learning for tabular data
- Active learning and deep probabilistic ensembles
- Image Search Take 2 - Convolutional Autoencoders
- An Overview of Attention Is All You Need
- Image search with autoencoders
Images
- Clustering and Image Segmentation
- Image Search Take 2 - Convolutional Autoencoders
- Image search with autoencoders
Industry
- Model Evaluation For Humans
- Monitoring Machine Learning Models in Production
- From Docker to Kubernetes
Learning to Rank
MLB
- Evaluating my 2020 MLB Predictions - Part 2, The Postseason
- Evaluating my 2020 MLB Predictions - Part 1, Pete Alonso
- MLB 2020 Postseason Projections
- Predicting Pete Alonso's 2020 Performance
- 2019 World Series Pitcher Matchups
- Deep learning for tabular data 2 - Debunking the myth of the black box
- World Series Projections
- Hierarchical Bayesian Ranking
- Hypothesis Testing For Humans - Do The Umps Really Want to Go Home
Machine Learning
- Scaling Predictions
- Clustering and Image Segmentation
- Know Your Trees
- Monitoring Machine Learning Models in Production
Model Evaluation
Model Evaluations
- Evaluating my 2020 MLB Predictions - Part 2, The Postseason
- Evaluating my 2020 MLB Predictions - Part 1, Pete Alonso
Monte Carlo
Online Learning
Opinion
- A Beginner's Guide to Why You Should or Shouldn't Be Using Kubernetes for Machine Learning (With Illustrations)
- Neural Networks Explained
- Keras Feature Columns
Projections
Recommendation Systems
- A Tutorial on Collaborative Filtering in sklearn
- Image Search Take 2 - Convolutional Autoencoders
- Image search with autoencoders
Software
Time Series
Variational Inference
World Series
docker
helm
keras
- Tensorflow 2 Feature Columns and Keras
- Image Search Take 2 - Convolutional Autoencoders
- Keras Feature Columns
- An Overview of Attention Is All You Need
- Image search with autoencoders
kubernetes
- A Beginner's Guide to Why You Should or Shouldn't Be Using Kubernetes for Machine Learning (With Illustrations)
- From Docker to Kubernetes
pandas
pymc
pymc3
- 2019 World Series Pitcher Matchups
- World Series Projections
- Hierarchical Bayesian Ranking
- Hypothesis Testing For Humans - Do The Umps Really Want to Go Home
sklearn
- A Tutorial on Collaborative Filtering in sklearn
- Clustering and Image Segmentation
- A fast one hot encoder with sklearn and pandas