20 Interesting Applications of Deep Learning with Python

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20 Interesting Applications of Deep
Learning with Python
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20 Interesting Applications of Deep Learning with
Python Applications of Deep Learning With
Python Below, we are discussing 20 best
applications of deep learning with Python, that
you must know. Let's discuss them one by one i.
Restoring Color in BW Photos and Videos With
Deep Learning, it is possible to restore color in
black and white photos and videos. This can give
a new life to such media. The ACM Digital Library
is one such project that colorizes grayscale
images combining global priors and local image
features. This is based on Convolutional Neural
Networks.
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Restoring Color in BW Photos and Videos
The Deep Learning network learns patterns that
naturally occur within photos. This includes blue
skies, white and gray clouds, and the greens of
grasses. It uses past experience to learn this.
Although sometimes, it can make mistakes, it is
efficient and accurate most of the times. Do you
know about important Deep Learning
Terminologies? ii. Pixel Restoration
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Pixel Restoration in Deep Learning
With deep learning, we can even zoom into a video
beyond its resolution. In 2017, researchers from
Google Brain trained a Deep Learning network to
predict faces from their low-resolution images.
The Pixel Recursive Super Resolution works on
photos to enhance their resolution to a great
extent. iii. Describing Pictures
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A group of people
Language Generating RNN
Vision Deep CNN
shopping at an
outdoor market
There are many
vegetables at the
fruit stand.
Describing Pictures in Python Deep Learning By
now, you have noticed how Facebook can tag photos
and Google can label them for easier search. Deep
Learning can describe all the elements in a
picture. A deep learning network can identify
many areas in an image and can describe each area
in words. This is using accurate English grammar.
iv. Changing Gaze in Photos
Changing Gaze in photos
A Deep Learning network can alter the direction
in which a person looks in a picture.
v. Real-Time Analysis of Behavior
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Real-Time Analysis of Behavior in python Deep
Learning
Deep Learning networks can recognize and describe
pictures, we know that. But they can also analyze
poses of people in these pictures. They can get
real-time insights about behaviors of people,
cars, and other objects. vi. Translation
Translation
It is now possible to translate text on images in
real-time. The Google Translate app can do this-
you hold your camera on an object and a deep
learning network OCRs the image to translate it.
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vii. Generating Pictures of Galaxies and Volcanoes
Generating Pictures of Galaxies and Volcanoes
Using Deep Learning with Python, astronomers can
create pictures of volcanoes and galaxies. Have a
look at Deep Learning vs machine Learning viii.
Creating New Images Creating New Images Pix2Pix
taught a deep learning network to perform
activities like creating real street scenes from
colored blobs, creating a map from a real aerial
picture, fill colors between edges of objects,
and even turn day scenes into night scenes. ix.
Searching for Text in Images and
Videos Searching for text in images and
videos The Oxford Visual Geometry group can
search for text in pictures and videos using deep
learning. It searches for text in BBC News
videos. Check out http//zeus.robots.ox.ac.Uk/text
search//search/ We searched for 'Glitter' and it
did a less than perfect job, but is accurate
pretty much most of the time.
x. Outperforming Humans in Computer Games
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Outperforming Humans in Computer Games
The Deep Learning community trains humans to beat
humans at games like Space Invaders, Pong, and
Doom. The computers learned the rules on their
own by playing for a few hours.
xi. Robotics
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Applications Deep Learning With Python - Robotics
With the capabilities of Deep Learning, robots
can get up when they fall, carry out tasks that
need them to be gentle, and even react to the
people who push them around. xii. Self-Driving
Cars
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Self Driving Cars
One name we've all heard is the Google
Self-Driving Car. Such vehicles can differentiate
objects, people, and road signs. These also make
use of the lidar technology.
xiii. Generating Voice
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Applications of Deep Learning With Python -
Generating Voice
Deep Learning networks like WaveNet by Google and
Deep Speech by Baidu can automatically generate
voice. They can learn to mimic human voices so
they can improve over time.
Let's revise Python Applications
xiv. Composing Music Like the previous
application, we can train a deep learning network
to produce music compositions. The computer
learns the patterns and statistics of artists and
creates a unique piece. xv. Restoring Sound in
Videos Restoring Sound in Videos Deep learning
makes it possible to restore sound in muted
videos. The computer can add sounds like
scratching objects with a drumstick. This uses
supervised learning. Apart from this, software
like LipNet can read people's lips with 93
success.
xvi. Handwriting
Handwriting in Python Deep Learning
With deep learning, computers can not only
produce digital text and art, it can handwrite. A
computer can have its own handwriting. You can
try it out here-
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http//www.cs.toronto.edu/araves/handwritina.cai
xvii. Deep Dreaming Deep Dreaming This Python
Deep Learning Application can enhance features in
images. Deep Dreaming makes the computer
hallucinate on the top of an image. This results
in dreamy images. xviii. Inventing and Hacking
own Crypto Google Brain has devised two neural
networks- one to generate a cryptographic
algorithm to protect their messages. The other
attempts to crack this. It performed well at
devising, but not so much at hacking it. xix.
Deep Learning Networks Creating Deep Learning
Networks Deep Learning Networks Deep Learning
products like Neural Complete can produce new
deep learning networks. It is written in Python
and is trained to generate code in Python. Let's
discuss Python Machine Learning Tutorial xx.
Writing Wikipedia articles, computer code, math
papers, and Shakespeare Long Short-Term Memory
(LSTM) is an architecture that can generate
Wikipedia-like articles, fake math papers, and
much more. Not all the times does this make
sense, but there will be progress. So, this was
all in Applications of Deep Learning With Python.
Hope you like our explanation. Conclusion Hence,
in this Python Deep Learning Tutorial, we have
tried to bring to you some of the most
interesting Applications of Deep Learning with
Python. Still, if you feel any query, you can ask
in the comment tab.