#Datawhale

Aimed at those with a background in machine learning, this resource outlines key aspects of recommendation systems including their overview, foundational algorithms, practical implementations, and interview preparations. It provides a comprehensive pathway from fundamental learning to hands-on practice and interview readiness. Detailed content covers essential algorithms, practical system building, and strategic insights. Connect with the community for continuous discussion and shared resources.

This guide provides an objective introduction to graph deep learning, combining insights from prominent works and university courses. Covering basic concepts, graph neural networks, and practical code using frameworks like NetworkX, DGL, and PyG, it's suitable for beginners and offers theoretical and practical exercises. Future updates will include applications in different fields, ensuring a comprehensive resource for those keen on graph deep learning advancements.

Key-book acts as a supplementary resource to 'Introduction to Machine Learning Theory' by Zhihua Zhou, offering clarity on complex machine learning concepts. It includes additional explanations, proof supplements, and case studies to aid in understanding the intricate mathematical aspects and abstract reasoning. Especially useful for those finding the original text challenging, this guide enhances comprehension and the learning process. View the latest updates and download the PDF online.

Explore Datawhale's open-source projects, designed to promote collaboration in AI through comprehensive guidelines and active community discussions. Get involved by joining ongoing projects or launching your own in our GitHub repository. The community supports every aspect of participation, from providing feedback to solving issues. Join today and contribute to the advancement of AI technology.

This tutorial, developed by Datawhale members, offers a comprehensive guide through practical exercises to enhance understanding of Agent technology. It is designed to support the 'Hands-On Learning in Agent Development' initiative, focusing on intelligent assistant creation with ModelScope Agent, while addressing general design strategies and potential applications. The tutorial features chapters on Agent principles, API-based practical implementation, and future outlook, equipping users with critical insights and practical skills for developing intelligent assistance applications.