Neural Networks

On this page, you can locate several tutorials. If you are looking for code, go to the Tutorials link, hover, and choose the code options that appear when you hover over the Neural Networks link.

Introduction to NN Concepts – Examples – XOR Part 1Download
NN Concepts_Review_LinearRegression_ActivationFunctions_Loss Functions Softmax_LogLikelihoodDownload
Back-propagation-in-neural-nets-Partial Derivatives – ANN – One Hidden Layer – Sigmoid – GatesDownload
Derivatives for one-vector input, ANN – Back PropDownload
Derivatives – Two input vectors – Back Prop – ANN_GatesDownload
Using-Softmax-and-Cat – Cross-Entropy-in-a-NN-Derivative-Softmax-with-Cross-Entropy-and-JacobianDownload
Examples in Softmax-Entropy -INfo Gain – One- Hot-Encoding-GatesDownload
Convolutional-Neural-Networks-GatesDownload
Introduction to RNN_Transformers_Attention – LSTMDownload
Reference: RNN_Book ChapterDownload
RNN_ – LSTM – Examples – _Derivatives for Back Prop RNN – Transfer_Attention_Memory_GatesDownload
Encoder-–-Decoder-NN-GatesDownload