Artificial Neural Networks course

Authors: Kenza Bouzid, Agnieszka Miszkurka, Tobias Höppe

Solutions for labs for ANN course at KTH. Each lab contains implementation of neural networks algorithms as well as notebooks with experiments.

Lab1 - From Single Layer to Multi Layer Networks

Part 1

perceptron learning, delta rule
Multi Layer Perceptron with generalised delta rule (backprop)

Part 2

Multi-layer perceptron network for chaotic time-series prediction

Lab2 - Radial basis functions, competitive learning and self-organisation

Function approximation with RBF Network
Radial Basis Function trained with competitive learing
Self-organising maps used for data cluserisation and visualisation

Lab3 - Hopfield networks

Hopfield network with Hebbian learnign
Experiments concerning various properties of hopfield networks such as:
- capacity
- convergence
- distortion resistance
- energy
- sequential vs batch update
- sparsity of patterns

Lab4 - Restricted Boltzmann Machines and Deep Belief Nets

RBM trained with contrastive divergence
DBN trained using greed layer-wise pretraining of RBMs
Using RBM and DBN for classification and generating new samples with MNIST data set