A curated list of research, applications, tutorials, and software built using the H2O open-source machine learning platform.
Awesome H2O is a curated directory of resources, projects, and research built using the H2O machine learning platform. It aggregates community-generated content like tutorials, academic papers, software, and courses to demonstrate H2O's real-world applications and ecosystem. The project helps users discover how H2O is applied across industries and learning contexts.
Data scientists, machine learning practitioners, researchers, and educators who use or are evaluating the H2O platform and want to explore its capabilities through community examples and resources.
It provides a centralized, community-vetted collection of H2O-related content, saving users time in discovering use cases, learning materials, and tools. Unlike generic ML resource lists, it focuses specifically on the H2O ecosystem, offering practical insights and inspiration directly tied to the platform.
A curated list of research, applications and projects built using the H2O Machine Learning platform
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
Aggregates diverse community content like blog posts, tutorials, and research papers in one place, saving users time from scouring multiple sources. The README organizes resources into clear categories such as Books, Courses, and Software.
Showcases practical H2O implementations across domains like healthcare, finance, and environmental science, providing inspiration and proof of concept. Examples include predicting employee churn and water quality using AutoML.
Includes university courses, presentations, and benchmarks to facilitate learning and comparative analysis. The Courses section lists offerings from institutions like UCLA and the University of Oslo.
Highlights tools and extensions built on H2O, such as the modeltime.h2o R package for forecasting and splash for MOJO file interfaces, demonstrating community-driven innovation.
The list relies on community contributions and may not be regularly updated, leading to broken links or stale resources. The README admits 'we are just getting started,' indicating possible gaps.
While curated, there's no explicit vetting for accuracy or relevance, risking inclusion of low-quality or obsolete content. Users must self-assess the value of each linked resource.
Missing search, filtering, or rating systems makes it difficult to find specific or high-quality content efficiently, relying solely on manual browsing of the markdown list.