Deep Learning

 Deep Learning

We all are amused by seeing our recent searched products and similar products on the other websites and apps while browsing them. Have you given a thought on how is it possible?

And also astonished by the working of Google assistant

These are the outcomes of Deep Learning and Artificial Neural Networks.

 Deep Learning

 Artificial Intelligence imitates human behavior, Machine learning deals with algorithms to perform the same, and Deep Learning is a branch of Machine learning that deals with a huge amount of data. 

Deep learning is a branch of Machine Learning, inspired by the structure of the human brain. The working of deep learning algorithms is similar to the human brain in analyzing data with defined logic. To attain this, a multilayered structure of algorithms called Neural Networks is used.

How DL Works?

The design of the neural network is based on the structure of the human brain. Just like how humans use their brains to recognize patterns and classify different types of information, neural networks can be taught to perform the same tasks on data. The first layer is called the input layer and the last layer is the output layer and all middle layers are called hidden layers. Each Hidden layer is composed of neurons, which are interconnected. The neuron will process and then propagate the input signal it receives the layer above it. The strength of the signal given to the neuron in the next layer depends on the weight, bias, and activation function.

Suppose we have to predict something, firstly, we feed the input to the input layer, then the input layer transfers this input to the hidden layer. Each neuron has some weight. 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.

The hidden layer sounds simple but involves lots of operations and calculations. 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 we are solving.

The network takes a vast amount of data and operates them in multiple layers; with an increase in the number of levels, the complexity also increases. 

Why Deep Learning is so popular?

Deep learning is enjoying enormous popularity due to its high accuracy concerning a huge amount of data. Nowadays, due to big data, a lot of innovative opportunities are in the field of deep learning.

The main reasons for using deep learning are-

1. A large amount of data

2. Complex problems

3. Feature Extraction

1. A  large amount of data:

       The main reason to use DL over ML is the vast amount of data.ML platforms perform 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. Here, Dl comes into the picture. DL can easily solve the problem no matter what is the size of the data. Deep Learning takes care of both structured and unstructured data.

2. Complex problems:

          DL can solve complex real-world problems. This is another reason why DL is taking preference over machine learning.

3. Feature extraction:

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

In deep learning, we 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.

Applications of Deep Learning:

DL is used in almost every field nowadays. 

1. Self-driving Cars– These cars use Dl algorithms to analyze the environment. Like to analyze the pedestrian, traffic lights, roads, and buildings as an object. Self-driven cars analyze those objects and then drive. This is nothing but Deep Learning.

        2. Military-Military systems armed with AI and Deep Learning are efficiently able to handle huge amounts of data and that makes up a critical part of modern warfare owing to effective computing and decision-making capabilities. During immediate threats, Deep Learning solutions streamline analysis and facilitate quick decision-making through critical insights.

        3. Medical Field- Deep learning is used in the medical field to detect tumors or cancer cells. How much the area covered by cancer cells or any more task deep learning is used

         4. Robotics- In robotics, you can use deep learning to identify the nearby atmosphere so that robots can walk and react accordingly.

         5. Customer support and translation-Nowadays, most companies use chatbox for customer service, this is created using DL. Translation from one language to another is also done using DL.