What is Deep Learning and Why it is Popular?

Do you wanna know What is Deep Learning(DL) and Why it is so much popular in almost every field? If yes, then read this full article and understand about deep learning and its functionalities. At the end of this article your, all doubts will be clear.

Hello, & Welcome!

In this blog, I am gonna tell you-

1. What is Deep Learning?
2. How DL work?
3. Why Deep Learning?
4. Where DL is Used?
5. Limitation of DL.
6. Deep Learning Frameworks.

Let’s get started-

What is Deep Learning?

Deep Learning is a subset of Machine Learning. That is, in turn, a subject of Artificial Intelligence. Artificial Intelligence is a technique that allows machines to mimic humans. Machine Learning is a technique to achieve the goal of AI with the help of algorithms to train the model.

DL is the type of machine learning which works based on human brains. In DL, the human brain is called an artificial neural network.

Let’s understand DL in a more precise way and see how it is different than machine learning.

How Deep Learning is different from Machine Learning?

Suppose you have to identify between oranges and Apples. So to perform this task in machine learning, you need to train the model and tell the features of apples and oranges. These features may be fruit color, shape, and many more. So the model will predict the output according to the features told by you.

But,

In DL, the neural network automatically picks up the features without your intervention. That’s the power of deep learning. In DL, most of the work done automatically by neural networks.

How DL Works?

The whole working of DL happens with three layers-

1. Input Layer.
2. Hidden Layer.
3. Output Layer.

As you can see in the picture, deep learning works in the same manner. Each circle you are seeing is a neuron. The first layer is the input layer. Suppose you have to predict something, then whatever data you have is given to the input layer. The last layer is the output layer, which gives the result of your prediction. Like in the example of apple and orange prediction, you will get the prediction as it is orange or apple in that output layer.

In between both layers, there is a hidden layer. This hidden layer is the same as neurons in the human brain. Like when you see something through your eyes, then it doesn’t automatically give the output. First, it processes through billions of neurons, then the result will come. This whole process happens in nanoseconds, so you can’t imagine. This is the whole concept behind the artificial neural network.

First, we feed the input to the input layer, then the input layer transfers this input to the hidden layer. Each neuron has some weights. And Each neuron has a unique associated number, which is called a bias. In the hidden layer, the main operation is performed, and after the processing of the hidden layer, you get an output.

Here, it sounds very simple, but in the hidden layer, lots of calculations and operations are performed. And one more thing. hidden layers may be thousand in number. In the picture, I have shown only one hidden layer but it may be a thousand, depending upon the problem you are solving.

Why Deep Learning?

The main three reasons for using DL is-

1. The huge amount of Data.
2. Complex problems.
3. Feature Extraction.

1. The huge amount of Data-

The very first reason to use deep learning over machine learning is a huge amount of Data. Machine Learning performs well in small size data, but when you supply a huge amount of data to the model, machine learning algorithms fail to solve the problem. That’s why DL comes into this place. DL can easily solve the problem no matter what is the size of the data.

In short, DL can deal with huge amounts of data, but machine learning can’t. DL can deal with both structured as well as unstructured data.

2. Complex Problem-

The complex problem is nothing but real-world problems. Machine learning was not able to solve those problems. Therefore, deep learning comes into the picture. DL can easily solve complex real-world problems. This is another reason why DL is taking preference over machine learning.

3. Feature Extraction–

In machine learning, you need to manually feed all the features related to your problem in order to train the model. Then after your model will predict the result based on the feature you fed. So if you have a real-world problem that consists of a huge number of features, then it is time-consuming as well as hard to do.

But,

In deep learning, you only need to give objects or data, no need to feed features manually. DL automatically generates the features of objects or data. DL learns feature then generate those features. And one more thing it generates only high order features which help to predict the output. This is the biggest reason why deep learning is very popular.

NOTE- When you have a small dataset problem, then you can use machine learning. But when you have a huge amount of data then prefer deep learning.

Where DL is Used?

DL is used in almost every field nowadays. But the most used areas of deep learning is-

1. Medical Field- Deep learning is used in the medical field to detect the tumor or cancer cells. How much the area covered by cancer cells or any more task deep learning is used.
2. Robotics- In robotics, you can use deep learning to identify the nearby atmosphere so that robots can walk and react accordingly.
3. Self-driving Cars– By using deep learning algorithms, these cars can analyze the environment. Like to analyze the pedestrian, traffic lights, roads, and building as an object. After analyzing those objects, the self-driven cars drive. This is nothing but a Deep Learning.
4. Translation– To translate from one language into another language is possible by using a deep learning algorithm.
5. Customer Support– Nowadays most of the companies are using a chatbot for their customer support. So this chatbot is created with deep learning. It replies and solves your problem in the same way as a human does.

Limitation of DL.

As I discussed, that to perform deep learning, you need a huge amount of data to train the model. And most of us have only the necessary amount of data capacity in our system. So all machines don’t have the capacity to deal with massive amounts of Data.

The next limitation is Computational power. DL requires GPU( Graphical Processing Unit) which has thousands of core as compared to CPU. These GPU’s are more expensive.

The next limitation is Training Time. The deep neural network takes hours or months to train the model. This training time depends on the data and the number of hidden layers. The more data and hidden layers, the more time it takes to train.

Deep Learning Frameworks.

The most popular frameworks of DL are-

1. TensorFlow
2. PyTorch
3. Keras
4. DL4J.
5. Caffe.
6. Microsoft Cognitive Toolkit.

I hope now you have a clear idea about DL, Why it is so famous and its application. If you have any questions, feel free to ask me in the comment section.

Enjoy Learning!

All the Best!

Learn How Neural network Works- here.

What is the Activation Function? just read it here.

Read Gradient Descent from here- Gradient Descent- Understand Completely in a Super Easy Way!

Read Stochastic Gradient Descent from here- Stochastic Gradient Descent- A Super Easy Complete Guide!

Are you interested to know How Artificial Neural Network Works, then read this blog- Artificial Neural Network.

Thank YOU!

Though of the Day…

‘ It’s what you learn after you know it all that counts.’

– John Wooden

Read Deep Learning Basics here.