41–44 of 44 posts
"You should understand the problem well enough to list some potential solutions to test first to find the best model" and "it shouldn't be used for a problem that conventional programming can solve" like hard coding a symbolic rule.
I've been hearing about deep learning revolutionizing "everything" for the past 8 years. So, can someone name me any significant impact it made on figuring out what to do about the recent epidemic? If not, I think it's worth reflecting on what value we get out of the technology that sucked up so much of our intellectual, financial and computational resources.
It's not revolutionizing everything in the field of AI. It's "only" good at prediction in domains where data is poorly structured, such as images, audio, video, text and some games (which is a lot to be fair, hence the publicity it got in the recent years). It's not that great on structured data or on small datasets, and is not really adapted to anything not related to raw, "black-boxed" prediction. I'm not sure what…
Here is a take from Google AI on when to use ML: https://developers.google.com/machine-learning/problem-frami... "You should understand the problem well enough to list some potential solutions to test first to find the best model" and "it shouldn't be used for a problem that conventional programming can solve" like hard coding a symbolic rule.
Earlier quoted context omitted.
The function would simply be a chain of multiplications and (typically) non-linear transformations. For classification problems, there is often a final non-linear transformation, like a softmax. Example with ReLu activation: Output_i = max((input_i * weight_i),0)
I don't understand. The example you gave is not an algebraic function.
In the case of a linear network, the function would be a dot product between the input and the weights: 𝑥1𝑤1+𝑥2𝑤2+𝑥3𝑤3 ... for all inputs (xi) and weights (wi)