  There is a great need for accurate and autonomous spectral classification
methods in astrophysics. This thesis is about training a convolutional neural
network (ConvNet) to recognize an object class (quasar, star or galaxy) from
one-dimension spectra only. Author developed several scripts and C programs for
datasets preparation, preprocessing and postprocessing of the data. EBLearn
library (developed by Pierre Sermanet and Yann LeCun) was used to create
ConvNets. Application on dataset of more than 60000 spectra yielded success
rate of nearly 95%. This thesis conclusively proved great potential of
convolutional neural networks and deep learning methods in astrophysics.
