Bayesian probability is used within the machine learning world and understanding Bayes’ rule at a high level is key.
Bayes’ rule relates the conditional probability of one event to the actual condition.
In Bayesian Learning, Bayes’ rule helps us to figure out the most probable hypothesis given an input space and some domain knowledge. The great thing is this can simply be expressed as, argmax Pr[h|D] h∈H, where D is the input space chosen from some probability distribution and h our hypothesis that is in the set of all possibile hypotheses, H. …
TL;DR The networking capabilities Docker-Compose abstracts for docker users has significantly helped my mental state and should help yours too! Stop using bash scripts and hardcoding IP addresses for your docker container network. It leads to hacky solutions and can be prone to errors.
Docker-Compose creates a single network for which all of its services can communicate over. This feature makes docker-compose an incredibly powerful tool. You will no longer have the self induced headache of remembering the IP address’ for all the containers you launched or creating your own docker network from scratch.
In this article, Ill show you…
My co-workers and I have built a full-stack web application with next.js, flask, postgres, and Grafana. Why do we need Grafana? We use it for specific visualizations that are hard to generalize in our main stack. It allows for customizable and version controllable dashboards that look great and can be embedded into web applications. This gives us the ability to add dynamic visualizations with ease.
I love applying software engineering and devops to modernize space applications!