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If the internet were around when microwave ovens were invented, there would have been much the same outcry. Deep learning is excessively simple and elegant, I can explain it in person in under 5 minutes to anyone who has done high school algebra.


Great. Explain why training converges in subexponential time despite the nonconvex cost function and does not overfit the data.


As you get closer to the right answer, the changes you need to make are smaller and harder to discover.


You only need to know that if you are studying the field. A good explanation of deep learning is no more surprising to the general populace than general relativity is (spacetime isn't actually the surface of a balloon).


That's not correct though, theres plenty of reasons why on an intuitive level neural networks should not work. Also, genetic algorithms are intuitively appealing but do not work well.


Genetic Algorithms are used in designing of antenna arrays.


Because in high dimensional space there are partial saddle points, not local minima. Intuitively obvious, and shown theoretically in the last 12 months.


Are you referencing https://arxiv.org/abs/1412.0233?


Shown theoretically under fairly artificial assumptions.




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