MIT

R0:fd224a04984225e4bdd2ae7a7e595529-Human Learn

Human Learn

Machine learning covers a lot of ground but it is also capable of making bad decision. We’ve also reached a stage of hype that folks forget that many classification problems can be handled by natural intelligence too. This package contains scikit-learn compatible tools that should make it easier to construct and benchmark rule based systems that are designed by humans. You can also use it in combination with ML models.

R0:2271580d5e4f655f084ee4605a50b147-labml.ai Deep Learning Paper Implementations

labml.ai Deep Learning Paper Implementations

This is a collection of simple PyTorch implementations of neural networks and related algorithms. These implementations are documented with explanations, and the website renders these as side-by-side formatted notes. We believe these would help you understand these algorithms better.

https://editorialia.com/wp-content/uploads/2020/05/machine_learning_from_scratch-2.jpg

Machine Learning From Scratch

An extensive list of fundamental machine learning models and algorithms from scratch in vanilla Python.