wandb

PyTorch implementation of the U-Net for image semantic segmentation with high quality images

High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)

Github repo with tutorials to fine tune transformers for diff NLP tasks

Transformers 3rd Edition

Phoneme Recognition using pre-trained models Wav2vec2, HuBERT and WavLM. Throughout this project, we compared specifically three different self-supervised models, Wav2vec (2019, 2020), HuBERT (2021) and WavLM (2022) pretrained on a corpus of English speech that we will use in various ways to perform phoneme recognition for different languages with a network trained with Connectionist Temporal Classification (CTC) algorithm.

Easiest way of fine-tuning HuggingFace video classification models

Generate Stunning Images and Craft Visual Stories for your Brand

Google's BigBird (Jax/Flax & PyTorch) @ 🤗Transformers

Automated question generation and question answering from Turkish texts using text-to-text transformers

Implementation for the different ML tasks on Kaggle platform with GPUs.

Exploring NLP weak supervision approaches to train text classification models. The project is also a prototype for a semi-automated text data labelling platform. Approaches: Snorkel and Zero-Shot Learning.

Utils and modules for Speech Language and Multimodal processing using pytorch and pytorch lightning

Train and fine-tune diffusion models. Perform image-to-image class transfer experiments.

Engage in a semantic segmentation challenge for land cover description using multimodal remote sensing earth observation data, delving into real-world scenarios with a dataset comprising 70,000+ aerial imagery patches and 50,000 Sentinel-2 satellite acquisitions.

Landcover classification on sentinel-2 data with Prithvi, EfficientNet-Unet and OSM / CNES Landcover labels.

Fine tuning Mistral-7b with PEFT(Parameter Efficient Fine-Tuning) and LoRA(Low-Rank Adaptation) on Puffin Dataset(multi-turn conversations between GPT-4 and real humans)