NN-lecturenotes - lecturenotes

Loss Functions and Fitting Models

lectures 5

We discussed linear regression, shallow NNs, and deep NNs where each represents a family of functions that map the input to output and farticular member of the family…

Shallow Neural Networks

lectures 3

Shallow NNs describe piecewise linear functions expressive enough to approximate complex relationships between multi-dimensional inputs and outputs[1].

Deep Neural Networks

lectures 4

Both shallow and deep NNs describe piecewise linear mappings from inputs to outputs with ReLU activation functions[1].

Supervised Learning Models

lectures 2

This note is a general introduction to DL concepts. I will be following the textbook by Simon J. D. Prince, the first reference. The first part of the textbook introduces…

Neural Networks or Deep Learning Fundementals

lectures 1-2

A neural network is a computational model that is inspired by the way biological neural networks in the human brain process information. The key element of this model is the neur…