In this video, we will start talking
about deep learning and how the recent
advances in the field have led to
amazing and mind-blowing applications.
I am sure that you are aware that deep
learning is one of the hottest subjects
in data science, if not the hottest,
especially with the tremendous amount of
fascinating projects that are surfacing
with the help of deep learning; projects
which people deemed almost impossible
with just a little over a decade ago.
Therefore, there is a lot of excitement
about deep learning. In this video, I will
share with you some amazing and recent
applications of deep learning that will
hopefully inspire and motivate you even
more about deep learning. The first
amazing application is color restoration,
where a given image in greyscale is
automatically turned into a colored one.
A group of researchers in Japan built a
system using convolutional neural
networks that can take a grayscale image,
like these ones, and add life to them by
turning them into colored ones. You can
find many other awesome examples by
following this link, which you can also
find below the video. Another really cool
but double-sworded application is speech
enactment, where an audio clip is
synthesized with a video, and the lip
movements in the video are synced with
the sounds and words in the audio clip.
Many attempts have been made in the past
to build such a system, but many of them
produced results that looked uncanny.
Recently, a group of researchers at the
University of Washington built the first
system that generates realistic results
by training a recurrent neural network
on a large corpus of video data of a
single person. The subject of their case
study was the former President of the
United States, Barack Obama. Let's look at
their example. So here is an audio clip
from one of Obama's speeches. "It's been
less than a week since the deadliest
mass shooting in American history." The
audio clip was synthesized with a
video of one of his other
speeches, and his lip movements were
synced with the words and sounds in the
audio clip. Let's take a look. "It's been
less than a week since the deadliest
mass shooting in American history." Anyone
watching the video can't really tell
that the video was synthesized. Not only
that, but their system is also capable of
extracting an audio from a video and
syncing the lip movements in another
video with the audio from the first
video. Let's look at an example of this.
"Especially our friends who were lesbian,
gay, bisexual, or transgender. I visited
with the families of many of the victims
on Thursday and one thing I told them is
that they're not alone." Your jaw dropped yet?
Well mine did, although I created this
slide and I knew what was coming. Another
fascinating application of deep learning
is automatic handwriting generation. Alex
Graves at the University of Toronto used
recurrent neural networks to design an
algorithm that can rewrite a given
message in highly realistic cursive
handwriting in a wide variety of styles.
So you can type some text in this field
and you can either select the style of
handwriting to be generated or let the
algorithm randomly select it for you.
There is a plethora of other
applications such as automatic machine
translation, where convolutional neural
networks are used to translate text in
an image on the fly. Another application
is automatically adding sounds to silent
movies, where a deep learning model uses
a database of pre-recorded sounds to
select a sound to play that best matches
what is taking place in the scene. Not to
leave out the popular applications of
classifying objects in images and
self-driving cars. For almost all of the
aforementioned applications, you heard me
say neural networks again and again. So
you must be asking haven't neural
networks been around for quite some time?
How come all of a sudden they are taking
off and becoming very popular with
endless applications? In order to answer
this question, let's start learning the
specifics of neural networks and deep
learning.