Roshchupkin shares ML GitHub practicals

Roshchupkin used an X post over the weekend to circulate a set of open machine-learning course materials built around GitHub notebooks and repository links. The post pointed readers to practical exercises on convolutional neural network explainability, autoencoders and representation learning, and framed them as hands-on summer-course materials for students to clone and run. (github.com) The public GitHub trail behind the post leads to a repository called `Advanced_ML_2026`. GitHub’s indexed description for that repository says it contains “Hands-on practicals for Advanced Machine Learning for Health Research — CNN explainability, autoencoders and representation learning.” (github.com 1) (github.com 2) That makes the post notable less as a product launch than as a distribution move: a social post directing learners to runnable code, notebook files and setup material already sitting in a public repository. ### What exactly was shared? The repository name visible in GitHub search results is `roshchupkin/Advanced_ML_2026`, and one of the notebook files surfaced in search is `02_Autoencoder_Latent_Space_Health.ipynb`. (x.com) GitHub shows that notebook as part of a public repository with more than 1,600 lines in the rendered file view. The X post referenced repository links and invited students to work through the material directly, according to the source briefing and the linked post reference. (github.com) The package described in the post included code, notebooks and a README with installation instructions. (x.com) ### Why do the topics matter for a practical course? CNN explainability, autoencoders and representation learning are three parts of a common applied ML workflow, especially in image-heavy or biomedical settings. GitHub’s description ties the material specifically to “Health Research,” which suggests the notebooks were organized around applied examples rather than only theory. (github.com) The autoencoder notebook title is the strongest concrete clue about the teaching format. `02_Autoencoder_Latent_Space_Health.ipynb` points to a lesson built around latent-space analysis, a standard way to teach how models compress and reconstruct data while exposing learned representations. (x.com) ### Where can readers verify the materials? Roshchupkin’s GitHub profile is public and lists 16 repositories, with a personal site also linked from the profile page. The profile confirms the account is active on GitHub and provides the main path for locating the materials outside X. (github.com) The repository itself is public, and the notebook file can be opened in GitHub’s browser view. Search indexing captured the repository description and notebook path even though the X page did not render readable text in the browser fetch. ### What does a student actually get from this kind of post? (github.com) The practical value is in the format. A public repository with notebooks lets students move from reading about CNN explainability or autoencoders to executing cells, changing parameters and inspecting outputs on their own machines. That is consistent with the post’s invitation to clone repositories and follow exercises step by step. (github.com) GitHub also preserves the file structure, which matters for course materials because it keeps code, notebook order and installation notes in one place. In this case, the visible notebook naming suggests a sequenced set of practicals rather than a single standalone demo. (github.com) ### What comes next for anyone who wants to use it? The next step is straightforward: readers can open Roshchupkin’s GitHub profile, locate `Advanced_ML_2026`, and start with the notebook files already indexed publicly, including `02_Autoencoder_Latent_Space_Health.ipynb`. The X post remains the entry point that ties those repository links together for students looking for the full set of practicals. (x.com) (github.com) (github.com)