Information regarding course evaluation
In order to pass the laboratory you need at least 35 points from 100
In order to pass the course you need at least 35 points from 100
The final grade is computed according the following formula: 0.6*(Laboratory Grade) + 0.4 *(Exam Grade)
Points for the laboratory part are obtained by presenting homework during the laboratory classes
Points for the course part are obtained at the end of the semester by passing the exam.
Laboratory Resources: https://github.com/Tensor-Reloaded/Neural-Networks-Template-2024
curs1 RN 2024.pptxCurs 2 - numpy.pptxnumpy
curs3 RN - perceptron.pptxcurs4 RN - gradient descent.pptxcurs5 - backpropagation.pptxCrossEntropy
SoftMax
Backpropagation
curs6 - weight initialization & overfitting.pptxWeight Initialization
Outher Activation Functions
Overfitting
Methods for reducing overfitting
curs7 - optimizers.pptxOptimizers
Curs 8 rn - pytorch.pptxcurs9 RN - Q Learning.pptxcurs10 - Convolutional.pptxConvolutional Neural Networks
curs11 2021 - actor critic.pptxReinforcement Learning: Policy gradient. Actor critic
curs12 - LSTM.pptxRecurrent Neural Networks. LSTM