Software
JAXGenesis: A JAX Library for Training Deep Generative Models
JAX Genesis: Implementations of different generative model architectures in the JAX framework using the Equinox JAX Library for neural network implementations.
Implemented Generative Model Architectures:
- Generative Adversarial Networks
- Variational AutoEncoders
- Flow-Based Models
- Energy-Based Models
- Neural SDEs
OmnixRL: A JAX Library for Deep RL
OmnixRL: A comprehensive reinforcement learning library implemented in JAX, inspired by OpenAI's Spinning Up. This library provides a clean, modular implementation of popular RL algorithms with a focus on research experimentation and serves as a research framework for developing novel RL algorithms.
Implemented Deep Reinforcement Learning Algorithms:
- Vanilla Policy Gradient (VPG)
- Trust Region Policy Optimization (TRPO)
- Proximal Policy Optimization (PPO)
- Deep Deterministic Policy Gradient (DDPG)
- Twin Delayed DDPG (TD3)
- Soft Actor-Critic (SAC)
MLAlgorithmsKit: A Light-weight Machine Learning Library in Python
Implementation of standard ML Algorithms from scratch using Numpy and Pandas libraries.
Algorithms Implemented:
Linear Regression, Logistic Regression, Multi-Layered Perceptron, K-Means Clustering, KNN (K-Nearest Neighbors), Decision tree