University of Tehran - Neural Networks and Deep Learning Course

This repository contains my solutions to homeworks and projects of the course "Neural Networks and Deep Learning" at University of Tehran in Fall 1397.

Course Topics

1. Introduction

• Natural Neural Networks
• Artificial NNs and Applications
• Architectures, Activation Functions and Learning in ANNs
• Mcculloch & Pitz Neuron

2. Classification and Regression Neural Networks

• Linear Perceptron
• AdaLine and MadaLine
• Multi-Layer Perceptron(MLP)
• Auto-encoders
• Restricted Boltzmann Machine
• Deep belief networks
• Convolutional Neural Networks(CNNs)
• Developments and Applications of CNNs

3. Memory Neural Networks

• Auto-Associative Net
• Hetro-Associative Net
• Iterative Auto-Associative Net.
• Hopfield Net
• Bidirectional Associate Memory
• Recurrent Neural Network(RNN)
• Long Short Term Memory (LSTM)
• Gated Recurrent Units(GRU)
• Developments and Applications of LSTMs and GRUs

4. Competition Learning Neural Networks

• MaxNet, Mexican-Hat, Hamming Net
• Self organizing Maps (SOM)
• Generative Adversarial Networks(GANs)
• Developments and Applications of GANs

References

1. “Fundamentals of Neural Networks” by Laurene Fausett, 1994.
2. “Deep Learning” An MIT Press book by I. Goodfellow, Y. Bengio and A. Courville , 2016.
3. Convolutional Neural Network(UFLDL Tutorial)/ available online at July 2016: http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork/
4. Convolutional Neural Networks (LeNet)/ available online at July 2016: http://deeplearning.net/tutorial/lenet.html