Scanning all new published packages on PyPI I know that the quality is often quite bad. I try to filter out the worst ones and list here the ones which might be worth a look, being followed or inspire you in some way.

abao-matrix-operations
Basic Matrix Operations

ai6
Artificial Intelligence Examples

DynEnv
Dynamic RL Environments for Autonomous Driving and Robot Soccer

func-adl.ast
Functional Analysis Description Language – Backend AST Manipulation Packages

grape-model
GRAPE makes it easy to fit a regression model with hyperparameter optimization.

pytorch-influence-functions
This package is a plug-n-play PyTorch reimplementation of Influence Functions. Influence Functions were introduced in the paper Understanding Black-box Predictions via Influence Functions by Pang Wei Koh and Percy Liang (ICML2017).

robotframework-imagelibrary
Image Processing Library For Robot Framework

tensorcox
Coxs partial likelihood in Tensorflow. Tensorflow implementation of Coxs’ partial likelihood [^1] based on a counting process representation[^2]. Combining Cox’s model with the counting process representation allows for more complex censoring patterns as well as the inclusion of time-varying covariates. Implementing this in Tensorflow with batch optimization methods gives us a powerful approach that can scale to big data problems and make full use of distributed computing environments. Since the algorithm is fully written in Tensorflow it is easy to integrate it into larger workflows and combine it with Neural Networks etc.

topologika
Localized topological data analysis

torchtuples
Training neural networks in PyTorch

trainer-xy
trainer dashboard

transparentai
Python tool to create an ethic AI from defining users’s need to monitoring the model.

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