#Jupyter notebooks

Discover a collection of Jupyter notebooks for machine learning, deep learning, and analytics, covering topics such as time series forecasting, computer vision, and anomaly detection. Explore distributed computing, generative AI, and techniques like hyperparameter tuning and recommender systems. Each notebook provides detailed examples, supporting data scientists and AI enthusiasts in applying Python to real-world business challenges.

Discover example Jupyter notebooks demonstrating how to set up AI/ML environments with SageMaker Studio Lab. This repository guides data scientists in areas such as computer vision and NLP, offering insights into project deployment with Amazon SageMaker. Explore community-driven content on geospatial data science and generative AI, and access diverse programming environments.

Discover detailed deep learning tutorials with notebooks covering PyTorch and JAX frameworks. Gain practical experience in optimization, transformers, and graph neural networks. Seamlessly run notebooks on Google Colab or locally, with pretrained models available. Explore concepts like Meta Learning and Self-Supervised Learning with clear guidance and community input.

Explore how converting Jupyter notebooks into plain text formats can improve version control and facilitate collaboration with Jupytext. This tool allows for editing in preferred IDEs and provides clear diffs for version control. Supporting several languages and formats like Python, Julia, and Markdown, Jupytext ensures easier editing and refactoring. Understand how paired notebooks synchronize `.ipynb` and text formats for a seamless workflow. Enhance collaboration on notebooks using Git, ensuring efficient edit-tracking in professional IDE settings.

Explore ready-to-run Jupyter notebooks for mastering the OpenVINO™ Toolkit, designed to facilitate efficient deep learning inference. This resource is suitable for beginners and experienced developers, offering step-by-step guidance for platforms like Windows, Ubuntu, and macOS. Benefit from extensive resources and community support while delving into OpenVINO's features. Ideal for AI projects with practical examples and optimization techniques.

Discover diverse machine learning projects and notebooks crafted for CloudxLab's educational courses. This repository offers a user-friendly platform for exploration and learning, featuring nbdime for improved Jupyter notebook version control. Engage with these projects to bolster expertise in cloud-based machine learning settings.