High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
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Updated
Jul 17, 2024 - Python
High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
Train and fine-tune diffusion models. Perform image-to-image class transfer experiments.
Implementation for the different ML tasks on Kaggle platform with GPUs.
Development for my M.Tech Thesis: Deep Learning Techniques for Road Segmentation in Indian Context
predicting showing up to doctor's appointment using mlp on imbalance dataset.
細胞検知モデル作成を行うチュートリアルスクリプト
Testing ray tune with slurm batch submission and optuna and wandb
hackable boilerplate for PyTorch Lightning driven deep learning research
Create a machine-learning algorithm that counts the number of grass-thatch, tin and other roofed houses in aerial (drone) imagery.
Transformers 3rd Edition
Machine Learning Lifecycle
PyTorch-Lightning Library for Neural News Recommendation
repo for training and experiments with skin-cancer dataset from kaggle
A convenient way to trigger synchronizations to wandb / Weights & Biases if your compute nodes don't have internet!
In this project we have developed a Deep Autoencoder using Dense Neural Networks to perform dimensionality reduction on MNIST and FMNIST datasets. The project includes training, saving, and evaluating models using PyTorch. Utilized the Weight & Biases library for monitoring and comparison of model performance
All Assignments of the course, Statistical Methods in AI at IIITH, Monsoon 2024
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