There are 2 repositories under weights-and-biases topic.
PyTorch implementation of the U-Net for image semantic segmentation with high quality images
๐ ๐ง๐ต๐ฒ ๐๐๐น๐น ๐ฆ๐๐ฎ๐ฐ๐ธ ๐ณ-๐ฆ๐๐ฒ๐ฝ๐ ๐ ๐๐ข๐ฝ๐ ๐๐ฟ๐ฎ๐บ๐ฒ๐๐ผ๐ฟ๐ธ | ๐๐ฒ๐ฎ๐ฟ๐ป ๐ ๐๐ & ๐ ๐๐ข๐ฝ๐ for free by designing, building and deploying an end-to-end ML batch system ~ ๐ด๐ฐ๐ถ๐ณ๐ค๐ฆ ๐ค๐ฐ๐ฅ๐ฆ + 2.5 ๐ฉ๐ฐ๐ถ๐ณ๐ด ๐ฐ๐ง ๐ณ๐ฆ๐ข๐ฅ๐ช๐ฏ๐จ & ๐ท๐ช๐ฅ๐ฆ๐ฐ ๐ฎ๐ข๐ต๐ฆ๐ณ๐ช๐ข๐ญ๐ด
Train a state-of-the-art yolov3 object detector from scratch!
Generic template to bootstrap your PyTorch project.
A convenient way to trigger synchronizations to wandb / Weights & Biases if your compute nodes don't have internet!
A clean and structured implementation of Transformer with wandb and pytorch-lightning
:dart: Deep Learning Framework for Image Classification & Regression in Pytorch for Fast Experiments
This repository focuses on training semantic segmentation models to predict the presence of floodwater for disaster prevention. Models were trained using SageMaker and Colab.
PyTorch Lightning Implementation of Diffusion, GAN, VAE, Flow models
Example codes in the medium post titled "Optuna meets Weights and Biases."
Tiny ResNet inspired FPN network (<2M params) for Rotated Object Detection using 5-parameter Modulated Rotation Loss
Deep learning based color transfer between images.
Order execution in the financial markets using Deep Reinforcement Learning.
Python framework for artificial text detection: NLP approaches to compare natural text against generated by neural networks.
:detective::robot: Monitoring a PyTorch Lightning CNN with Weights & Biases
MLOps Implementing "Brain Computer Interface" on Kubernetes
Training a racecar with W&B!
Bees CNN Algorithm (A Fuzzy Evolutionary Deep Learning)
A template repository to quickly get started on a new Machine Learning project.
Solution to the SETI Breakthrough Listen Competition hosted on Kaggle
Annotated Notebooks to dive into Self-Attention, In-Context Learning, RAG, Knowledge-Graphs, Fine-Tuning, Model Optimization, and many more.
Using GPT-3 to help me get more ๐ on medium.com
Predicting Turbine Energy Yield (TEY) using ambient variables as features.
Machine learning and AI projects require managing diverse data sources, vast data volumes, model and parameter development, and conducting numerous test and evaluation experiments. Overseeing and tracking these aspects of a program can quickly become an overwhelming task.
Recommendation Engine powered by Matrix Factorization.
This project focuses on semantic segmentation using the BDD100K dataset, a large-scale, diverse dataset for autonomous driving. The main objective is to accurately segment and identify various objects in street scenes, which is important for improving the perception capabilities of autonomous vehicles.
Monitor SLURM jobs using Weights and Biases (wandb) ๐
Step into the realm of Emotion Detection Generalization! Uncover the power of deep learning as we decode human emotions from facial images. Explore our arsenal of fine-tuned CNN models and curated datasets, shaping a future where technology empathizes and connects on a deeper level. Revolutionize human-computer interaction with us today!
Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning (ICML 2022)
Teaching transformer-based architectures
medium blog posts code
My solution for the Automathon Superposed MNIST competition.
Automating machine learning model comparisons using Weights & Biases based on GitHub issues, enabling collaboration and communication.
Experiment tracking tools example
Official Course Webpage for CS175: Projects in AI