If you are into python, machine learning, graph data, statistical learning, basic graph neural network architectures, or just want to get a hands-on and ground-up introduction to a new, actively-developing field to keep your chops sharp, we hope this might be a good book for you.
My colleagues and I recently had our textbook on network machine learning published with @universitypress.cambridge.org, available at a.co/d/e7LIVce. #networks#Python#machinelearning#@statistics#graphs
Following a lot of hard work and dedicated effort from countless parties, our textbook providing a hands-on introduction to analyzing network data is now available for pre-order from Cambridge University Press www.cambridge.org/core/books/h...
Thanks for your positive thoughts @ar0mcintosh.bsky.social -- on our next updates, we will majorly take this feedback about the title into consideration :) appreciate the rec!
We tie together our contribution by discussing the implications of causal mindsets on deriving valid inferences, and how these mindsets may inform computational neuroscience efforts going forward. (4/4)
Using working examples drawn from multiple domains of experimental and observational neuroscience, we illustrate how analytic strategies can produce erroneous or misleading conclusions that struggle to generalize, even if the goal is not to develop an explicitly causal or mechanistic model (3/4)
Everybody knows that correlation does not equal causation. But how many people actually know how to apply that thinking? In this review, we provide a ground-up introduction to the motivations, assumptions, and methods that are essential when making causal inferences (2/4)
See our new preprint focused on causal thinking for data analysis in neuroscience #causal#neuroscience#neuroimaging#datascience#poldracklab with Brian Caffo, Maya Mathur, and @russpoldrack.bsky.social (1/4)
