  Deep learning and deep architectures are emerging as the best machine
learning methods so far in many practical applications such as reducing the
dimensionality of data, image classification, speech recognition or object
segmentation. In fact, many leading technology companies such as Google,
Microsoft or IBM are researching and using deep architectures in their systems
to replace other traditional models. Therefore, improving the performance of
these models could make a strong impact in the area of machine learning.
However, deep learning is a very fast-growing research domain with many core
methodologies and paradigms just discovered over the last few years. This
thesis will first serve as a short summary of deep learning, which tries to
include all of the most important ideas in this research area. Based on this
knowledge, we suggested, and conducted some experiments to investigate the
possibility of improving the deep learning based on automatic programming
(ADATE). Although our experiments did produce good results, there are still
many more possibilities that we could not try due to limited time as well as
some limitations of the current ADATE version. I hope that this thesis can
promote future work on this topic, especially when the next version of ADATE
comes out. This thesis also includes a short analysis of the power of ADATE
system, which could be useful for other researchers who want to know what it is
capable of.
