implementing a transformer to evaluate tweets
we implement a transformer architecture to predict whether tweets are worthy or subpar
we implement a transformer architecture to predict whether tweets are worthy or subpar
we set up a net with convolutional architecture in pytorch, which can distinguish images of birds from those of airplanes
we construct a neural net for sentiment prediction from scratch using numpy
we use a convolutional network to predict the inputs and outputs of a separate target net
we use a historical example to illustrate a graphical tree approach to bayes' equation for updating beliefs in light of new evidence