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Hello Chinasa, glad to have you here. Lately, the area of quantization is beginning to gain traction in deep neural networks (DNN), what's your take on this exciting new domain and do you think the tradeoff between the marginal drop in accuracy for a reduced model size is worth it?
Also, with my experience in DNN, I noticed that most people see DNN as a one stop point to solve almost any classification or Regression tasks (This should normally not be the right thing to do, as one may be going to a stick fight with a bazooka), so my question is this, at what point do we draw the line between opting for a more traditional ML algorithm/technique or diving right into using neural nets.
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New York City, United States
I have no experience in quantization, so I won't be able to answer this well. However, I believe it is important that ML be as accessible as it possibly can be and am looking forward to seeing improvements within this domain.
I think it's totally fine to explore as many techniques as you can since you don't know what works until you try it!
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