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SELECTIVE magical thinking has bred a mountain of erroneous publications whose primary motivation is to see the hand of god in nature. https://lnkd.in/dffiSnf View in LinkedIn

BREAK WITH THE PAST. "Perfection and progress were abstractions that had no place in Darwin's pragmatic and relativistic scheme." Yet there were many nods towards the older (and religious) viewpoint. https://lnkd.in/dqgy8Hd View in LinkedIn

MAGICAL THINKING. "Fabicconi numbers in nature, selective and seductive.” https://lnkd.in/dHb3uBz View in LinkedIn

PREVAILING PERFECTION VIEW. "The concept of perfection in the natural world was integral to pre-Darwinian rationalizations of nature as evidence of the designing hand of a providential Creator." https://lnkd.in/dqgy8Hd View in LinkedIn

TRANSCENDENTALIST VIEW. "Every particular in nature, a leaf, a drop, a crystal, a moment of time is related to the whole, and partakes of the perfection of the whole." (Ralph Waldo Emerson). https://lnkd.in/dXaHRqi View in LinkedIn

VAST DATA. "The factors very quickly become too numerous for humans to keep track of. Not a breakdown of logic per se, but the effects of sloppy shortcuts, bad handwriting, and immense volumes of code.” https://www.reddit.com/r/todayilearned/comments/3d3vct View in LinkedIn

LIMITED BY VISUALIZATION. "Sometimes with neural networks on images you can display the weights in the form of an image and see it select certain parts of the image but beyond that we don't have good ways of finding out what the weights mean." https://www.reddit.com/r/todayilearned/comments/3d3vct View in LinkedIn

NEURAL NETWORKS. "These weights get refined by training algorithms. The classic being back propagation. You hand the network an input chunk of data along with what the expected output is. Then it tweaks all the weights in the network. Little by little the network begins to approximate whatever it is you're training it for." https://www.reddit.com/r/todayilearned/comments/3d3vct View in LinkedIn

NODES AND WEIGHT. "Neural networks work as a bunch of nodes (neurons) hooked together by weighted connections. Weighted just means that the output of one node gets multiplied by that weight before input to the node on the other side of the connection. These weights are what makes the network learn things." https://www.reddit.com/r/todayilearned/comments/3d3vct View in LinkedIn

"NEURAL NETWORKS are called black boxes because the training algorithm finds the optimal node values to solve a problem, but looking at the solution it is impossible to tell why that solution works without decomposing every element of the network. In other words, the node values are extremely sensitive to the context (nodes they connect to), so you have to map out the entire thing to understand it." https://www.reddit.com/r/todayilearned/comments/3d3vct View in LinkedIn