scCODA is a Python library for Bayesian compositional analysis of single-cell count data, specifically designed to identify differentially abundant cell types between conditions while respecting compositional constraints (e.g., cell type proportions sum to 1). The core implements a Dirichlet-Multinomial model with spike-and-slab priors, providing posterior inclusion probabilities and effect size estimates. It integrates with the scverse ecosystem (anndata, scanpy) for data handling and uses TensorFlow Probability for HMC/NUTS sampling. The package also includes wrappers for alternative methods (ALDEx2, ANCOM, etc.) and provides utilities for data conversion, visualization, and synthetic data generation. While the repository is well-architected with clear separation of concerns and includes tutorials and unit tests, it is explicitly marked as no longer maintained (successor in pertpy using JAX). The technical drift is high due to outdated TensorFlow/TPP dependencies and lack of active development.
The repository 'ml-road' is primarily a collection of educational resources for machine learning, deep learning, and agentic AI, including curated links to courses, books, and recent topics. It also contains two small demo applications: one for Chinese character recognition using a convolutional neural network with batch normalization (TensorFlow 1.x) and another for MNIST handwritten digit recognition using a softmax regression (TensorFlow 1.x). The applications are runnable as command-line tools but require specific data directory structures and are tightly coupled to global flags, limiting their reusability. The codebase shows signs of technical drift due to reliance on deprecated TensorFlow 1.x APIs.
This repository is a Chinese-language chatbot built with Rasa 1.10.18, a now-deprecated version of the Rasa framework. It implements a simple customer service bot for telecom queries (phone bill and data usage) using a BERT-based NLU pipeline (HFTransformersNLP with bert-base-chinese) and a rule-based dialogue management system with story-driven flows. The codebase is minimal: a single custom action for querying consumption, a few intents and entities, and a set of dialogue stories. The project is functional but lacks tests, CI/CD, or modern Rasa practices. The pinned dependencies (rasa==1.10.18, transformers==2.4.1) are severely outdated, and the code relies on deprecated Rasa components. While the repository has accumulated stars (983) due to its accompanying blog series, the actual code is a basic Rasa template with little reusable craftsmanship. It serves as a learning example for building a Chinese chatbot with Rasa 1.x, but is not suitable for production or reuse without significant modernization.