Do you think the robot in the above picture saying is correct?
Have you ever felt something like this will happen in the coming future?
Can the machine brains overpower the thinking power of human brains?
Do you even think that machines can think like humans?
Confused? Or totally confused?
Just wait! By the end of this blog, you will get all your questions answered.
Nowadays, Deep Learning is giving state-of-the-art results all across the world. There is no doubt in saying that it has become the prominent technologies so far and will continue conquering the world like this in the coming future.
I visualize a time when we will be to robots what dogs are to humans, and I’m rooting for the machines.
You might be thinking how? Keep thinking! You will definitely reach up to a result.
In this blog, you will figure out the amazing facts regarding Deep Learning. Also, it will give you reasons which will attract you towards this.
1. Automatic Coloration of Black and White Images
Images used to be black and white initially. Then came the tradition of colorful images. The task of changing b/w images to colored was done by hands. Now, it is done using Deep Learning. It uses the images and texts in the photographs to color the image similar to the human operator. Generally, to recreate the images, large Convolutional Neural Networks, and supervised layers are used. The same approach can also be used to colorize still frames of black and white movies.
2. Automatically Adding Sounds to Silent Movies
This is a very interesting application of RNN and CNN. In this, the task is to add sound to the silent movies. This task is done using training 1000 videos having drumsticks sound striking different surfaces and creating different sounds. These videos are then used by the deep learning models to predict the best-suited sound in the video. Then, to predict if the sound is fake or real, a Turing-test like setup is built.
3. Automatic Machine Translation
Sometimes, some text is needed to be translated into another language. For this, a manual translator is appointed. But, deep learning is best for Automatic Translation of Text and Images.
Text translation is done using implementing the dependencies of the words and then mapping to the new language. This translation is done using stacked Neural Networks and large LSTM RNN. Also, images are translated using CNN in which the image is recreated using translating the text written in it in the required language.
4. Object Classification and Detection in Photographs
As the name says, the object is detected in the images and identified by the previously known objects. This detection can achieve amazing results using CNN. We can detect the objects at a crowded place and identify them using the data-set fed in the model.
5. Image Recognition
This is one of the best applications of deep learning where using previously fed data, it identifies the objects and people in the image. This is one of the amazing applications used in various sectors like gaming, media, tourism, retail etc. Just think about it. One of the real-life examples of image recognition is your identification when you upload any of your photos on Facebook.
6. Automatic Handwriting Generation
Ever imagined our college and school assignments done by a translator which could even mimic our handwriting. No! Then, think about it once.
This can be done using a Deep Learning model which have a corpus of hand-writings and generate new handwriting for a given word or phrase. From this corpus of hand-writings, the relationship of pen movement and letters is learned. What if we could generate forensic hand-writings and mimic them.
7. Automatic Text Generation
What if we write a line and the further lines could be generated by the machine itself. In this model learned word by word or character-by-character and generate further text by itself.
This can be achieved by relating items in the input strings sequence and then generate text. In recent times, LSTM model is achieving heights in this area.
8. Automatic Image Caption Generation
Nowadays, the caption for images needs to be perfect and up-to-date and going with the trend. If done manually, the caption may not be that captivating. But, if a machine does this work for you, then? Yes, the machine can generate captions for images.
For this, large CNN is used to detect objects in the image and then LSTM to turn the labels to the coherent text.
9. Automatic Game Playing
What if a model plays computer games by itself just like a human does. Yes, this work can also be done using Deep learning models. This very task can be easily seen in AlphaGo game. This is the domain of deep reinforcement learning.
10. Self-Driving Cars
Ever imagined a car driven by itself. Yes, such type of cars is made by companies in which a complete self-blown car like Google’s is made. Companies use a large amount of data for training. Now, when human senses are used to drive a car, companies are thinking of digital sensors to do the same.
Constant learning and then replicating that learning accomplishes this task.
It is very important to target the audience for our advertisement. Advertising is a key application of Deep learning as by that we can predict a data-driven advertising and precisely targeting the audience. This is the need of the hour for the companies across the world as the investment will only be done on that particular audience.
12. Predicting Earthquakes
Harvard scientists are using deep learning models to predict the timing of earthquakes. This timing is very important to save the lives of people.
13. Deep Learning in Healthcare
Some brain diseases like brain cancer are difficult to cure as the invasive brain cells are not easily spotted. During operations using neural networks in conjunction with Raman spectroscopy allows them to detect the cancerous cells easier and reduce the post-operation of residual cancer. This sometimes takes the lives of people.
The Deep learning models are making our life easy-going and less complicated. Using these models, we can do our work in less time and efforts with much better accuracy and efficiency. In the upcoming blogs, you will read about the various Neural Networks associated with Deep Learning.