"AIDD-LSD1-BNN-VAE" is a comprehensive repository hosting a variety of machine learning models and data processing techniques, created using Colaboratory. This project focuses on advanced implementations of Bayesian Neural Networks (BNNs), Variational Autoencoders (VAEs), and hybrid models in the context of data-driven research.
This repository includes a wide range of Jupyter notebooks and Python scripts, each demonstrating different aspects of data processing and machine learning:
0714.ipynb, AIDD_vae_数据预处理.ipynb, etc.: Notebooks for data preprocessing and exploratory analysis.
AVI_Final.ipynb, BNN_ense.ipynb, etc.: Implementation of Bayesian Neural Networks and ensemble methods.
Deep Ensemble.ipynb, Dropout_12x_3_AIDD.ipynb, etc.: Techniques for model ensemble and dropout analysis.
GRAPH_lorenz_2.ipynb, Hybrid_Modeling.ipynb, etc.: Advanced modeling techniques including graph models and hybrid systems.
SGLD_!!!.ipynb, SGLD_Hybrid.ipynb, etc.: Stochastic Gradient Langevin Dynamics (SGLD) applications.
Train_Split.ipynb, data_processing.py, etc.: Scripts for training data splitting and processing.
cGAN_hybrid_final.ipynb, bbp_hetero_overfitting.ipynb, etc.: Various approaches to generative modeling and overfitting analysis.