Richard Michael – Medium

Richard Michael

Richard Michael
in
Towards Data Science

Infinitely Wide Neural-Networks | Neural Tangents Explained

use Python and JAX to efficiently build infinitely wide networks for deeper network insights on your finite machine.

Dec 14, 2020

Infinitely Wide Neural-Networks | Neural Tangents Explained

Dec 14, 2020

Richard Michael
in
Towards Data Science

Optimizing Hyperparameters the right Way

Efficiently exploring the parameter-search through Bayesian Optimization with skopt in Python. TL;DR: my hyperparameters are always better…

Sep 30, 2020

Optimizing Hyperparameters the right Way

Sep 30, 2020

Richard Michael
in
Towards Data Science

Millennial Suicides — an Analysis about Change

A simple, yet meaningful Pyro model to uncover change-points over time.

Aug 5, 2020

Millennial Suicides — an Analysis about Change

Aug 5, 2020

Richard Michael
in
Towards Data Science

Improve Your Model with Missing Data | Imputation with NumPyro

Make the best of missing data the Bayesian way. Improve model performance and make comparative benchmarks using Monte Carlo methods.

Jul 28, 2020

Improve Your Model with Missing Data | Imputation with NumPyro

Jul 28, 2020

Richard Michael
in
Towards Data Science

3 Probabilistic Frameworks You should know | The Bayesian Toolkit

Build better Data Science workflows with probabilistic programming languages and counter the shortcomings of classical ML.

Jul 20, 2020

3 Probabilistic Frameworks You should know | The Bayesian Toolkit

Jul 20, 2020

Richard Michael
in
Towards Data Science

Single-Parameter Models | Pyro vs. STAN

Modeling U.S. cancer-death rates with two Bayesian approaches: MCMC in STAN and SVI in Pyro.

Jul 15, 2020

Single-Parameter Models | Pyro vs. STAN

Jul 15, 2020

Richard Michael
in
Towards Data Science

10.000 hours in Data Science | Gaining Proficiency

The way from data novice to professional.

Jun 26, 2020

10.000 hours in Data Science | Gaining Proficiency

Jun 26, 2020

Richard Michael
in
Towards Data Science

Compute the Incomputable | How SVI and ELBO work

One reason why Bayesian Modeling works with real world data. The approximate light-house in the sea of randomness.

Jun 22, 2020

Compute the Incomputable | How SVI and ELBO work

Jun 22, 2020

Richard Michael
in
Towards Data Science

How your model is optimized | Know your Optimization

The answer to: “Why is my model running forever?” or the classic: “I think it might have converged?”

Jun 15, 2020

How your model is optimized | Know your Optimization

Jun 15, 2020

Richard Michael
in
Towards Data Science

Pyro Top-Down Forecasting | Application-case

Connect the dots over time and forecast with confidence(-intervals).

Jun 8, 2020

Pyro Top-Down Forecasting | Application-case

Jun 8, 2020

Richard Michael

Richard Michael

I am a Data Scientist and M.Sc. student in Bioinformatics at the University of Copenhagen. You can find more content on my weekly blog http://laplaceml.com/blog

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