It all started in the year 2016 when the most powerful Deep Learning algorithm created by Deep Mind Labs that was later acquired by Google beat the reigning Go world champion Lee Sedol. So in this blog post we are delving into the details of Deep Learning, what it means, its huge implications to the society and such other aspects in order to get to know one of the most important technologies of the 21st century.
Watch this Artificial Intelligence Tutorial for Beginners video
So what exactly is Deep Learning?
It is a set of radical new technology, tools, algorithms and extraordinarily different ways of getting things done. It is about machines becoming cognizant and taking over the tasks which were hitherto reserved only for humans due to their far superior capabilities in terms of intelligence as compared to any machine.
At the most basic level it mimics the human brain in terms of its structure. It is modeled on the basis of the human brain. The human brain is the most complex single thing existing in the known universe. So to mimic that is not a child’s play. That is one of the reasons why Artificial Intelligence domain had remained stagnant for over five decades even though the term AI was coined in the post-world war 2 era.
The Deep Learning apparatus consists of the following:
- A multiple layer of artificial neurons simulating the human brain
- The neuron connections getting stronger or weaker depending on input data
- The observed data is generated by the interaction of the different layers
- Multiple types of training depending on the requirement
- Data that could either be labeled or unlabeled
- The machines getting smarter with each set of data
- The ultimate stage is the machines getting cognitive
Let us start with the simple deep learning model and how to go about training your deep learning apparatus. Within the Deep Learning universe we have convoluted neural network, regression neural network, and such other differences in the way the final conclusion is reached via the network of deep learning technologies.
With the advent of Deep Learning we can put machines to work for a lot of tasks like computer vision, image recognition, natural language processing, driving autonomous vehicles, predicting the eventuality of a certain occurrence in business and so on and so forth.
In a supervised learning methodology a lot of data will be input to the system so that the machine can identify whether the conclusion that it has arrived at is right or wrong thanks to the labeling of data that is provided. In unsupervised machine learning there is no labeling and hence the machine has to figure out for itself if a certain decision was right or wrong thanks to the huge quantities of data that is fed to the system. Then there is something called as semi-supervised learning which fits in somewhere between the supervised and unsupervised learning.
Some of the practical applications of Deep Learning:
How do you think Facebook recognizes the millions of images that users post on its site without much of a human intervention? It is machine learning at work that goes through millions of images at one go to find out what is there in each image with heightened accuracy. It then labels those images as per the conditions that are imposed for segregating the images.
Read how deep learning is taking over our world in this insightful blog
The machines can even look at a certain image and give it a caption based on the constituents of that image. This has been tried and tested and the machines are currently doing fairly well and it can only get better with time. They can generate a symphony, add elements that are missing in a certain image with very good accuracy.
It is also possible for machines to read handwriting once it is fed with enough of handwriting so that it can come up with its own conclusions predictions and make sense of different types of handwritings.
Another important field of the power of Deep Learning is the field of natural language processing which makes sense of the verbal conversation that people have with their machine counterparts. It is fairly good at understanding various accents to come up with highly accurate results.
Now that you know some of the major implications of the Deep Learning technology, you might as well check out the Intellipaat Deep Learning training and master this awesome field!
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