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Would you say it's interesting to explore after spending much time on them ? Do you feel like one could make an use for it pragmatically within certain context
by 6r17 1y ago
Would you say it's interesting to explore after spending much time on them ? Do you feel like one could make an use for it pragmatically within certain context or it's way too much of a toy where most of the time getting a service / coherent llm would ease-in the work ?
- AnotherGoodName 1y agoYes. I think learning them and learning their limitations is the best way to learn neural networks actually. Give a class of students an image with horizontal lines where every second line is a solid color and every other is random static. See how their left to right markov chains do here (should make ~50% correct predictions). Then rotate the image 90degrees. Have the class observe a left to right markov chains gets 0% when predicting this (every second pixel being random will do that). What to do? Maybe input both ways and weight towards the best one with a perceptron? Hey first step to learning a neural network! From there you can iterate more and more until you no longer really have markov chains but instead neural networks with a type of attention mechanism.