Understanding the learning algorithm that’s used with neural networks involves a fair dose of mathematical notations. This chapter details some relevant theoretical aspects of the way that neural networks operate. We will therefore review the notions of loss functions and gradient descent. Note that this chapter is by no means a complete description of how networks learn. As indicated at the end of this chapter, many other people have done an excellent job of accurately describing the theoretical foundation of learning and optimization mechanisms. Instead, this chapter is meant ...
© Alexandre Bergel 2020
A. BergelAgile Artificial Intelligence in Pharohttps://doi.org/10.1007/978-1-4842-5384-7_44. Theory on Learning
Alexandre Bergel1
(1)
Santiago, Chile
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