Student, who passed the course satisfactorily will be able to:
- Describe brain-like computing and differences of neurocomputers from conventional digital computers,
- Understand various aspects of neurocomputers such as deterministic, probabilistic and spiking neurons, feed forward and recurrent neural networks, deep structures of feed forward and recurrent neural networks, deep learning algorithms for training them.
- Applying deep learning algorithms to solve realistic problems and measure their performances.