Welcome to the ML-DL Model Zoo! This repository is a comprehensive collection of machine learning and deep learning algorithms, showcasing a variety of models used in the field of artificial intelligence.
This repository serves as a centralized location for various machine learning and deep learning models implemented using Python and PyTorch. It aims to provide clear, well-documented implementations to help you understand and apply these models in your projects.
Folders:
generative_models: Contains implementation of generative models.
interpretability/: Contains models and scripts focused on model interpretability and explainability. These tools help in understanding how models make decisions and the factors influencing these decisions.
transformers/: Includes implementations of transformer models, which are widely used in natural language processing (NLP) tasks for their powerful sequence-to-sequence capabilities.
Files:
generative_models/gan.py: Implementation of a GAN from scratch.
generative_models/C_gan.py: Implementation of a Conditional GAN from scratch.
generative_models/CNN_gan.py: Implementation of a GAN from scratch, that uses CNN layers.
interpretability/vision_transformer.py: Core implementation of interpretability techniques for vision transformers.
interpretability/notebook.ipynb: Jupyter notebook demonstrating interpretability methods and their applications.
transformers/transformer_model.py: Implementation of the transformer model architecture.
transformers/training_script.py: Script for training transformer models on various datasets.
README.md: This file, providing an overview and guidance on the repository.
LICENSE: License file under the Apache-2.0 License.
Prerequisites:
Python 3.7+
PyTorch 1.8.0+
torchvision
numpy
matplotlib