I did the "early years" of both statistics and tiny neural networks/perceptrons in college a long time ago. It also helps that I use math at work (anything from simulated 3D physics to DSP.)
Since then, I've used Wikipedia and Mathworld when work had needed it. Regression, random forest, simulated annealing, clustering, boosting and gradient ascent are all on the statistics/ML spectrum.
But the best resource was running NVIDIA DIGITS, training some of the stock models, and really looking deeply at the visualizations available. You could do this on your own computer, or these days, rent some spot GPU instance on ECC for cheap.
I highly recommend going through the DIGITS tutorials if you want a crash course in deep learning, and make sure to visualize all the steps! Try a few different network topologies and different depths to get a feel for how it works.
Since then, I've used Wikipedia and Mathworld when work had needed it. Regression, random forest, simulated annealing, clustering, boosting and gradient ascent are all on the statistics/ML spectrum.
But the best resource was running NVIDIA DIGITS, training some of the stock models, and really looking deeply at the visualizations available. You could do this on your own computer, or these days, rent some spot GPU instance on ECC for cheap.
I highly recommend going through the DIGITS tutorials if you want a crash course in deep learning, and make sure to visualize all the steps! Try a few different network topologies and different depths to get a feel for how it works.