What is Deep Learning and how does it work? (An Analysis)

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The answer to the question of “What is Deep Learning” is the innate ability of machines to learn about unstructured data in an unsupervised or supervised pattern. 

Deep learning is the branch of Artificial intelligence and a very crucial part of Machine Learning. Neural networks are the main component of deep learning. The advancement that the older learning algorithms have seen, the broad use of artificial neural networks, creating representation, and understanding of the data is what Deep Learning is all about.

In this blog we are going to know about How does Deep Learning Work?, the Types of Deep Learning, Where and When can we use Deep Learning?, the applications of Deep Learning and why does it matter? So you’ll get to know a whole idea about Deep Learning?

HOW DOES DEEP LEARNING WORK?

Deep learning works with the help of neural networks. Neural networks are networks designed by using patterns and algorithms such that they function just like a human brain. Therefore you must have heard how Artificial intelligence acts as an artificial equivalent of the human brain. 

So there are three layers of neural networks:

  1. INPUT LAYER
  2. HIDDEN LAYER
  3. OUTPUT LAYER

Input layers receive all the data that has to be analyzed. 

The hidden layer is the layer where all the mathematical functions, algorithms, and understanding of the data are done. This layer can be considered the most complex one. 

The Output layer has to just provide the output or the prediction that has been finally made by the machine using the two layers before. 

TYPES OF DEEP LEARNING

There are 6 types in total of Deep learning models present. All these types cater to different conditions of perception, analysis, and structure of input & output. Let’s take a look at them now:

SUPERVISED LEARNING

  1. Classic Neural Networks (Multilayer Perceptrons): It is the most basic type of deep learning model. It has a singular nature present that allows it to adapt to other binary patterns through the simulations of human brain learning patterns.
  2. Convolutional Neural Networks (CNNs): CNN is the advanced version of classic neural networks. They were initially introduced for image data processing, and are the most efficient ones for image classification.
  3. Recurrent Neural Networks (RNNs): RNN’s main function includes prediction sequences. LSTM (i.e LONG-SHORT TERM MEMORY) is one of the many popular algorithms of Recurrent neural networks.

UNSUPERVISED LEARNING

  1. Self-Organizing Maps (SOMs): SOMs are used for dimensionality reduction, for example, if the inputs are more than 2 then they’ll be reduced to 2 dimensions. 
  1. Boltzmann Machines: In Boltzmann machines, it does not focus on one parameter, but on multiple parameters in a hyperspace circular way. This kind of unsupervised learning is used while working with a specific dataset.
  1. AutoEncoders: Autoencoders as the name suggests encodes the input provided, performs functions for the activation, and then decodes that data for output. It helps to find some inherent structure that might exist inside the data.

WHERE AND WHEN CAN DEEP LEARNING BE USED? 

Deep Learning is among the most sought after techniques in the wide world of computing and technology. Hence there are so many areas where it finds usage, let us take a look at the areas of application for Deep Learning. 

  1. Complex calculations to determine the efficiency of input/output relation 
  2. Image processing: Face detection and recognition –  Criminal authorities use these techniques to find quick traces of culprits. You know, classic Mission: Impossible style! 
  3. Fast processing for possible converting of 2D images to 1D images  
  4. The model requires greater flexibility
  5. Situations where there is no output involved 
  6. To understand the framework of a model
  7. Building higher-level recommendation systems 
  8. Feature detection/recognition
  9. Classification and regression problems
  10.  To hasten the pace of processing 

15 APPLICATIONS OF DEEP LEARNING IN REAL WORLD

Below are the 15 applications that are being stormed by deep learning technology and have successfully made advancement too:

  1. VIRTUAL ASSISTANTS
  2. ENTERTAINMENT
  3. HEALTHCARE SERVICES
  4. FRAUD DETECTION
  5. PIXEL RESTORATION
  6. IMAGE RECOGNITION
  7. AUTOMATIC HANDWRITING GENERATION
  8. SELF DRIVING CARS
  9. VOICE SEARCH 
  10.  ROBOTICS
  11.  PREDICTING NATURAL DISASTERS 

 13. CANCER DETECTION

14. MARKET PRICES 

15. FINANCES 

WHY IT MATTERS?

Why is there such importance for deep learning in today’s world? What is its importance? 

All these questions might have popped up while you would have gone through topics such as Artificial Intelligence or Machine Learning. As whenever there is a decision on these topics, a huge part is hidden while mentioning the word “Deep Learning” (such a profound description of the concept, deep isn’t it?).

Deep learning is taken seriously, and it matters in the real world because of its accuracy, precision, and trustworthiness that it has gained over the years through its algorithms, outcomes, and efficiency.

The power and potential of Deep Learning to manage complex computing processes and heaps of data are what has endeared it to the technologists of today. 

The proliferation of Deep Learning in our lives proves how essential it is in today’s world to keep pace with the technological developments the world has witnessed.

We hope you liked this article and got to know a lot more about What Deep Learning is and what it is all about. You can check out Verzeo for the best internship courses on computer science among many other disciplines. 

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