Sometimes I get asked how to learn computional methods, and there is quite a lot of great, free reading material and tools to get you started (or go further).
Sometimes I get asked how to learn computional methods, and there is quite a lot of great, free reading material and tools to get you started (or go further).
These first two are probably the best books on Machine Learning that I have read. I highly recomend them both
Deep Learning (Ian Goodfellow and Yoshua Bengio and Aaron Courville): https://www.deeplearningbook.org
Information Theory, Inference, and Learning Algorithms (David Macay) http://www.inference.org.uk/mackay/itila/
This books is also fantastic
Pattern Recognition and Machine Learing (Christopher Bishop) https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf
Doing Bayesian Data Analysis (John Kruschke) https://sites.google.com/site/doingbayesiandataanalysis/what-s-new-in-2nd-ed
Bayesian Data Analysis (Andrew Gelman, et al) http://www.stat.columbia.edu/~gelman/book/
I use Keras typically. I would recomend this as it has a lot of very reasonable defaults for most uses. Pytorch is also very good but for those who want more control.
I use pymc3 personally, which is python based.
Stan is computationally equivalent, but more compatable with r.