DagsHub is an AI platform for managing the machine learning lifecycle, covering dataset curation and annotation, experiment tracking, and model management. It supports unstructured and multimodal data such as text, images, audio, video, and documents. The platform is built on open source tools and formats. DagsHub states it does not currently provide compute resources and relies on the user's own infrastructure.
A book offering a comprehensive introduction to central ideas underpinning deep learning, intended for newcomers to machine learning and those already experienced in the field. It covers concepts relating to contemporary architectures and techniques and includes a self-contained introduction to probability theory. Hardback, PDF eBook, and a free-to-use online version are listed as access options.
The caret package provides a set of functions in R intended to streamline creating predictive models. Its tools cover data splitting, pre-processing, feature selection, model tuning using resampling and variable importance estimation, and it offers a uniform interface and standardized common tasks.
A weekly news and interview podcast covering the Kubernetes community, including release discussions, projects, and AI policy topics. Episodes feature guests from the Kubernetes ecosystem and are hosted as numbered installments with accompanying show notes.
A Gary Marcus article on his Marcus on AI Substack asking when the GenAI bubble might burst and how that could happen within 12 months. It cites survey findings on generative AI and security, notes $50B in and $3B out, and argues that hallucinations remain and that the industry is built on hype.
Meaning of is the personal blog of Kenny Bastani, covering open source software, graph databases, cloud native architectures, information theory, artificial intelligence, and machine learning. As of September 18, 2025, the blog is no longer maintained, with readers directed to the author's GitHub or LinkedIn profiles.
A blog and article site by Meta S. Brown about predictive analytics. It addresses executives, business analysts, IT staff and data analysts, describing how each group can understand analytics, work with analysts, and communicate technical topics to decision makers.
Community notes
Be the first to share how this project worked for you.
Sign in with a verified account to leave a review or join the discussion.
Sign in to reviewKeep exploring
A book offering a comprehensive introduction to central ideas underpinning deep learning, intended for newcomers to machine learning and those already experienced in the field. It covers concepts relating to contemporary architectures and techniques and includes a self-contained introduction to probability theory. Hardback, PDF eBook, and a free-to-use online version are listed as access options.
The caret package provides a set of functions in R intended to streamline creating predictive models. Its tools cover data splitting, pre-processing, feature selection, model tuning using resampling and variable importance estimation, and it offers a uniform interface and standardized common tasks.
A weekly news and interview podcast covering the Kubernetes community, including release discussions, projects, and AI policy topics. Episodes feature guests from the Kubernetes ecosystem and are hosted as numbered installments with accompanying show notes.
A Gary Marcus article on his Marcus on AI Substack asking when the GenAI bubble might burst and how that could happen within 12 months. It cites survey findings on generative AI and security, notes $50B in and $3B out, and argues that hallucinations remain and that the industry is built on hype.
Meaning of is the personal blog of Kenny Bastani, covering open source software, graph databases, cloud native architectures, information theory, artificial intelligence, and machine learning. As of September 18, 2025, the blog is no longer maintained, with readers directed to the author's GitHub or LinkedIn profiles.
A blog and article site by Meta S. Brown about predictive analytics. It addresses executives, business analysts, IT staff and data analysts, describing how each group can understand analytics, work with analysts, and communicate technical topics to decision makers.
