  We present a convolutional neural network, designed in the auto-encoder
configuration that can detect and denoise astrophysical gravitational waves
from merging black hole binaries, orders of magnitude faster than the
conventional matched-filtering based detection that is currently employed at
advanced LIGO (aLIGO). The Neural-Net architecture is such that it learns from
the sparse representation of data in the time-frequency domain and constructs a
non-linear mapping function that maps this representation into two separate
masks for signal and noise, facilitating the separation of the two, from raw
data. This approach is the first of its kind to apply machine learning based
gravitational wave detection/denoising in the 2D representation of
gravitational wave data. We applied our formalism to the first gravitational
wave event detected, GW150914, successfully recovering the signal at all three
phases of coalescence at both detectors. This method is further tested on the
gravitational wave data from the second observing run ($O2$) of aLIGO,
reproducing all binary black hole mergers detected in $O2$ at both the aLIGO
detectors. The Neural-Net seems to have uncovered a pattern of 'ringing' after
the ringdown phase of the coalescence, which is not a feature that is present
in the conventional binary merger templates. This method can also interpolate
and extrapolate between modeled templates and explore gravitational waves that
are unmodeled and hence not present in the template bank of signals used in the
matched-filtering detection pipelines. Faster and efficient detection schemes,
such as this method, will be instrumental as ground based detectors reach their
design sensitivity, likely to result in several hundreds of potential
detections in a few months of observing runs.
