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Implementing CLIP With PyTorch Lightning
Manan GoelAug 17Articles, Computer Vision, CLIP, COCO, Experiment, Panels, Plots, Tables, Github, PyTorch Lightning
Writing a Training Loop in JAX and Flax
Soumik Rakshit, Saurav MaheshkarJul 29Articles, Computer Vision, Object Detection, CIFAR10, CNN, JAX, Tutorial, Advanced
How to Train ML Models with SageMaker Studio Lab, a Powerful Jupyter-based ML Platform
Thomas CapelleJun 30Articles, SageMaker, Tutorial, Intermediate, Experiment, Semantic Segmentation, Autonomous Vehicles, U-Net
Integrating Keras with Weights & Biases
Justin Tenuto, Ayush ThakurFeb 18Beginner, Computer Vision, Classification, Keras, W&B Meta, Artifacts, Panels, Plots, Tables, MedMNIST
How to Build a Robust Medical Model Using Weights & Biases
Aman AroraFeb 17Intermediate, Computer Vision, Classification, PyTorch, Experiment, W&B Meta, Github, Artifacts, Panels, Plots, Sweeps, Tables, Kaggle, Health Care, Exemplary
Matrix Factorization from Scratch in JAX: Regularized SVD for Recommendation Systems
Bryan BischofFeb 07Advanced, Tabular, JAX, Experiment, Tutorial, Plots, Sweeps, MovieLens, Recommender Systems
Configuring W&B Projects with Hydra
Adrish DeyFeb 03Beginner, Computer Vision, Classification, Hydra, Tutorial, Panels, Sweeps, CIFAR10, MNIST
Optimizing Models with Quantization-Aware Training in Keras - Part II
Ishan DuttaJan 26Intermediate, Computer Vision, Keras, scikit-learn, Experiment, ResNet, Github, Plots, Kaggle
Transfer Learning: Serial versus One-time Training
Maria RodriguezJan 21Intermediate, Computer Vision, Object Detection, Transfer Learning, PyTorch, fastai, Experiment, Github, Plots, COCO
Optimizing Models with Post-Training Quantization in Keras - Part I
Ishan DuttaJan 21Intermediate, Computer Vision, Keras, scikit-learn, Experiment, ResNet, Github, Plots, Kaggle
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