Trying to understand Deep Learning but all the resources I’m finding are like: “and inside this black box is where the magic little goblin twists his dials and out comes your probability!”
Ugh.
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There’s a great series called “Neural Networks from Scratch in Python” and it shows you how to build the black box. Highly recommend it.
I’m currently programming a neural network to play monopoly against AI players with particular play styles and learn the best overall strategy, plus adding some moral detection to determine strategies for best overall (all players) score.
It’s hard to say exactly what’s going on in any given black box, because it depends on the task. Let’s set an example and talk about it.
Let’s say we’re training an algo to look for bananas in a picture. A picture is a 2D grid of RGB pixels, we can do math on those. One neuron may be programmed to light up if it detects the color yellow, another to detect a certain brightness of the picture, another (further along the chain) to recognize the shape of a set of yellow pixels in a 2d grid. All of these things are done mathematically.
There are also sets of biases (dials) built into these neurons. These biases increase the “importance” of the neuron in the final outcome of accurately detecting the banana during training. For example, say the network runs a million times, but in the successful triggers that yellow detector is much more valuable than one that looks for a different color or something… that dial gets a boost so that when the network runs on unknowns, that neuron’s input is valued more highly.
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