Saturday, June 1, 2019

Math for Deep Learning

TensorFlow and Keras make a deep learning (DL) programming easier. The supervised DL program’s
basic steps are in general; load data, define neural network model, compile the model, fit or train for loss
function parameters. It’s done in that order and pretty simple with those libraries.

However, logic behind those libraries is real math; Geometry for vector operations, Derivative and
differentiation for loss functions,  more specifically, stochastic gradient descent, chaining derivatives,
reverse-mode differentiation, and symbolic differentiation. And statistics of course.

More you understand those math, more deeply you can understand DL and use those libraries more
effectively.

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