20 Interesting Applications of Deep Learning with Python
Free Python course with 25 real-time projects Start Now!!
1. Top Python Deep Learning Applications
Today, in this Deep Learning with Python Tutorial, we will see Applications of Deep Learning with Python. In this tutorial, we will discuss 20 major applications of Python Deep Learning. So far, we have seen what Deep Learning is and how to implement it. Now let’s find out all that we can do with deep learning using Python- its applications in the real world.
So, let’s start exploring Applications of Deep Learning with Python.
2. 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 B&W 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.
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
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
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
A Deep Learning network can alter the direction in which a person looks in a picture.
v. Real-Time Analysis of Behavior
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.
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.
vii. 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
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
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/textsearch/#/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
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.
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
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
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
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.
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-
xvii. 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 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.
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.
See also –
Python Machine Learning Environment Setup