There are 4 lab sessions.
Main objectives :
- to design and apply networks in classification, function approximation, and generalization tasks
- to identify key limitations of single-layer networks
- to configure and monitor the behavior of learning algorithms for single- and multi-layer perceptrons networks
- to recognize risks associated with backpropagation and minimize them for robust learning of multi-layer perceptrons.
Main objectives :
In this lab, we have used an RBF network to approximate one- and two-dimensional functions. And we have developed a competitive learning algorithm to automate the process of RBF unit initialization. Furthermore, We have implemented the core algorithm of SOM and used it for three different tasks.
Main objectives :
- Understand the principles underlying the operation and functionality of auto-associative networks
- Train the Hopfield network
- Study the attractor dynamics of Hopfield networks the concept of the energy function
- Understand how auto-associative networks can do pattern completion and noise reduction
- Investigate the question of storage capacity and explain features that help increase it in associative memories
Main objectives :
- Understand the learning process of RBMs
- Apply basic algorithms for unsupervised greedy pretraining of RBM layers and supervised greedy pretraining of DBN
- Design multi-layer neural network architectures based on RBM layers for classification problems
- Study the functionality of DBNs including generative aspects