What is deep learning and why is it in demand?

The human brain is complicated and for all the right reasons. A pathway of neurons, millions of cells, creating a response to stimuli and observation, all of this working simultaneously to help our brain give an appropriate response. Furthering its greatness, the brain is constantly observing and learning. 

A well-known learning method- ‘learning by example’ helps us develop an attitude or coping mechanism towards something we haven’t fully experienced but have a lot of information about. When we use the same logic for machines, deep learning comes closest to this. 

What is deep learning?

Stemming out of the machine learning algorithm category, deep learning is a computer or a machine’s ability to learn things by example or data. It eliminates the need for manual feature extraction as it learns features directly from the data. 

The most common example would be automated vehicles. An automated car is able to distinguish between people walking on the road, poles, traffic signals, and signboards by understanding the data it receives. How does it manage to detect things? 

How does deep learning work?

Unlike traditional learning, deep learning is an artificial neural network that uses many layers to form features from the data it acquires. This data includes image, text, and sound which helps the machine form a clearer picture of the object to easily detect it.  Does this remind you of something? Yes, that’s exactly how our brain works! However, we naturally possess a neural network. 

Why is deep learning so much in demand today? 

As we move to an era that demands a higher level of data processing, deep learning justifies its existence for the world. 

One major defining moment for it would be the use of artificial neural networks which brings out the best outcome. Unlike machine learning, there is no need to build new features and algorithms because deep learning directly identifies features from the data. It uses 150 layers of information to process features directly from the data received and also monitor its own performance. 

Companies that are investing in deep learning are primarily looking to solve complex problems and this form of learning accomplishes that with its collection of large data sets. 

Another reason deep learning thrives in the world today is that it powers the functions that need voice and image detection. Companies that require data on face recognition, object identification, voice-to-speech application, and translation can make optimal use of deep learning techniques. 

Concluding…

While we are not exempted from the fact that deep learning works best only with a huge amount of data and takes time to be set up, there is hope for better performance. 

Moving from a structured and fixed architecture to an ever-evolving one, the next few years will see a rise in businesses moving to this new form of machine learning. Based on the existing data and examples of success, there seems to be an indication that companies using deep learning techniques perform better than ones that don’t.