Neural-Network

These are just assorted notes for now, which shall become something ready to be formalized. Non-bullshit The objective is to train a NN which captures subtle recurrent patterns among many well-chosen (and well-defined) features. The proper set of features that, in turn, captures the most relevant aspects of reality is what determines the distinction between a modest success or a total failure of this ML approach. All the features should be actual “measurements” of something real, like “Open Interest” or the “Long/Short ratio” and other obvious measurements like “Volume”....

Overview A valid (less wrong) intuitive metaphor is that we “learn” a “surface” which will match (will cover, up to the last wrinkle) the whole actual Himalaya. This notion “generalizes to any number of dimensions” meme (differences, distances and derivatives do not care about Mind’s abstract bullshit) The Himalayas (Truth) has to be “out there”. A good generalization is a bucket-sort, which can be thought off as a specific example of a classification problem....