Tutorials
2 minute read
Fundamentals
The following tutorials take you through the fundamentals of W&B for machine learning experiment tracking, model evaluation, hyperparameter tuning, model and dataset versioning, and more.
Use W&B for machine learning experiment tracking, model checkpointing, collaboration with your team and more.
Track, visualize, and compare model predictions over the course of training, using PyTorch on MNIST data.
Use W&B Sweeps to create an organized way to automatically search through combinations of hyperparameter values such as the learning rate, batch size, number of hidden layers, and more.
Track your ML experiment pipelines using W&B Artifacts.
Popular ML framework tutorials
See the following tutorials for step by step information on how to use popular ML frameworks and libraries with W&B:
Integrate W&B with your PyTorch code to add experiment tracking to your pipeline.
Visualize your Hugging Face model’s performance quickly with the W&B integration.
Use W&B and Keras for machine learning experiment tracking, dataset versioning, and project collaboration.
Use W&B and XGBoost for machine learning experiment tracking, dataset versioning, and project collaboration.
Other resources
Visit the W&B AI Academy to learn how to train, fine-tune and use LLMs in your applications. Implement MLOps and LLMOps solutions. Tackle real-world ML challenges with W&B courses.
- Large Language Models (LLMs)
- Effective MLOps
- W&B Models
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