RAPIDS: Open GPU Data Science | Scipy 2019 Tutorial | Scopatz, Becker, Kraus, Gama Dessavre

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The RAPIDS suite of open source software libraries gives you the freedom to execute end-to-end data science pipelines entirely on GPUs. RAPIDS is incubated by NVIDIA® based on years of accelerated data science experience. RAPIDS relies on NVIDIA CUDA® primitives for low-level compute optimization, GPU parallelism, and high-bandwidth memory speed through user-friendly Python interfaces. This tutorial will teach you how to use the RAPIDS software stack from Python, including cuDF (a DataFrame library interoperable with Pandas), dask-cudf (for distributing DataFrame work over many GPUs), and cuML (a machine learning library that provides GPU-accelerated versions of the algorithms in scikit-learn).

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Is it possible to find said notebooks somewhere? They are not available on the linked website in the description. I con only find similar but not identical notebooks on github in the repositories:
rapidsai/notebooks
rapidsai/notebooks-extended

fn