#notebooks

Discover the third edition of Practical Deep Learning for Coders with fastai1 notebooks in the 'nbs' folder. Note that these notebooks aren't compatible with the latest fastai version; for an updated version using the newest fastai, another repository is available. This guide is ideal for deep learning enthusiasts looking to elevate their coding skills with a practical and fastai1-focused approach.

This repository offers diverse resources like code samples and notebooks for developing and managing AI workflows with Google Cloud's Vertex AI. It supports both beginners and experts in utilizing its AI platform, providing guides on model development, deployment, and explanation. The community can contribute to enhance these resources, making it an evolving hub for machine learning exploration.

Access a full set of resources including notebooks, slides, and spreadsheets for the 2022 Practical Deep Learning for Coders course. This collection includes cleaned notebooks, Excel files, and Jeremy's slide decks designed for deep learning. Guidance for operating notebooks in GitHub Codespaces is also available. Explore course.fast.ai for comprehensive lessons and content to enhance understanding of deep learning.

Delve into a carefully selected collection of example notebooks showcasing Haystack's varied capabilities, such as vector databases, model providers, and retrieval methods. Find step-by-step demonstrations on retrieval enhancement, custom component integration, metadata enrichment, and advanced techniques in Haystack from version 2.0 onwards. This repository provides practical insights for diverse applications, including legal document analysis, custom documentation QA, and multilingual RAG pipelines, empowering contributions to a collaborative initiative.

Discover Jupyter notebooks tailored for Vertex AI that transition software engineers from basic to advanced machine learning levels. The repository provides various model architectures including DNN, CNN, and RNN, as well as resources for efficiently deploying models using TensorFlow, PyTorch, and Scikit-learn on Google Cloud. Users can enhance their skills via structured labs, solutions, and comprehensive setup instructions. Emphasizing hands-on experience with TensorFlow, it is well-suited for beginners. Contributions are currently limited to Google employees, fostering internal growth.

Explore the Lightning Library for a curated set of PyTorch Lightning tutorials that prioritize efficiency and reproducibility. Utilizing rich script formats, this repository ensures easy collaboration and environment consistency by supporting edits through Python scripts and conversion tools like jupytext. It offers a structured approach to contribution, dataset management, and valuable development insights, making it perfect for those looking to seamlessly create and manage tutorials in PyTorch Lightning.