In the field of artificial intelligence, big data, and analytics a new term-Deep learning-has steadily crawled its way in the business language.
It has been making its impact felt in areas like natural language processing, computer vision, audio recognition, and bioinformatics among many others.
- Deep learning is a sub-field of machine learning
- Deep Learning acquired the label “deep” on account of the many layers involved
- Computer vision and other forms of AI are made possible with the help of Deep Learning
- Deep learning can either be supervised or unsupervised
- Artificial neural networks are generally used in Deep Learning
But what exactly is deep learning? It is a machine learning technique that takes its inspiration from the learning approach that humans use to gain a certain type of knowledge.
It is a field of machine learning that uses the algorithms of neural networks which are originally inspired by the structure and functioning of the human brain.
The following are the five important things to know about deep learning:
Many a time people use the terms artificial intelligence (AI), machine learning, deep learning, and cognitive computing interchangeably.
Artificial intelligence is a wide category in the field of computing that deals with training computers to simulate human thinking.
Machine learning is a field of Artificial Intelligence that essentially involves training computers to do certain tasks without human intervention, i.e., without explicit programming.
Deep learning is a narrower category of machine learning that uses hierarchically stacked algorithms of increasing complexity and abstraction.
In deep learning, the computer is fed a huge set of data marked with Meta tags. The program processes the training data to create a feature set for the object to be identified.
Deep Learning derived its name from the number of iterations needed to process the data to approach an acceptable level of accuracy.
In Deep learning, the output of one layer is fed as input to the next. The algorithms start with preparing a generalized feature set, and then in the successive iterations, the feature set becomes more specific.
For example, let’s say we want the system to recognize dogs. The computer system is then first fed a set of images tagged “dog” or “not dog”.
It starts with first creating a feature set recognizing animals as a body with four legs. In the next iterations, details such as a tail would then be added.
Over time, after many iterations, the feature set for a dog would be created for the system to be able to recognize a dog accurately.
Computer vision is the processing capability of a computer system to recognize objects from their shape and size.
The previous example is particularly suitable because computer vision is one domain where Deep learning is exhaustively used.
Similarly, Deep Learning is used in applications designed for natural language processing, recommendation engines, text mining, and analytics.
All these use cases have one thing in common and that is, it is hard to define logic rules specifying what to look for.
We often come across supervised and unsupervised learning while talking about Deep Learning. It’s conveniently assumed that supervised means with human intervention, and unsupervised means that the computer trains on its own.
In all machine learning applications, the system will need to be trained by feeding in a lot of data.
Supervised learning demands that the data that is fed be tagged to help the system recognize the objects. However, unsupervised learning is when the computer is given a lot of images without tags.
In such a case, the computer will not be able to recognize objects but will be able to group similar images together.
Unsupervised learning is useful in cases when your data isn’t labeled or in cases like that of data mining when you don’t know what you are looking for.
‘Artificial neural networks’ is a term that is frequently discussed when it comes to Deep Learning. The networks are an interconnected group of nodes similar to the one in the human brain.
The nodes, known as neurons, are interconnected and transmit signals to one another simulating the functioning of a human brain.
Deep learning has these neurons arranged in layers transmitting information signals between different layers.
Since it takes inspiration from the functioning of a human brain, the applications include a wide (and ever-increasing) variety of applications which include imitating the working of a human brain.
With infinite possibilities for its applications and its growing popularity, we can expect to see deep learning be a part of our day-to-day lives soon.
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