In the 21st century, we are discovering new ways to communicate with each other, finding new transport methods for saving time, doing research in finding new renewable resources for creating fossil-free energy. In the same way, researchers are now moving forward in the field of data interpretation with the help of neural networks.

Now you might be thinking neural networks are the term related to the human brain, what could be the use of the neural network in data science? Well, in truth, neural networks are a part of the human brain, and their primary function is to transfer the information gathered by different senses to our brain. Furthermore, neural networks are a combination of neural cells that adapts according to the input. As a result, big MNCs use this technique to find out the relevant information in their massive database.

So today we’ll be answering the question of how neural networks are related to data science in a simple way so your brain cells don’t have to do extra time.

Neural network Algorithms

The neural network mimics the working of the human brain. Likewise, the neural algorithm searches for the relationship between the different data sets. There are Different Types of Neural Networks. If you look ten years back, you will find out the inception of neural networks from artificial intelligence.

The Neural Architecture

Neural network architecture consists of a three-layer along with few 100 nodes, but it depends on the usage of the network. You can add multiple layers in between to for heavy processing tasks. Given below we have defined the three main layers of neural network for your better understanding of the architecture.

Input layer

1. As the name suggests this layer takes in the information from outside. In this part of the Neural network, the actual learning and recognition of patterns in data happens here.
2. Furthermore, this layer relays the value taken in from each of its single nodes and passes it to the hidden layer. Also, the input layer is passive all the time. As a result, they don’t modify the data.

Output Layer

1. This layer provides all the output from the network. All the information that you will get from the network system will come out from this layer.
2. This layer works as an active state. Thus, manipulation of the data which came from the input layer.
3. The capacity for solving a problem or finding a pattern highly depends on the hidden and output layer.

Hidden Layer

1. This layer is located between Input and the output layer. The only job of a hidden layer is to transfer the data from the input layer to the output layer so it can interpret the pattern.
2. Every single value from the input layer is duplicated and sent to the hidden layer, which is why it is called the interconnected structure.
3. The values that enter this node get multiplied by the number of weights present in the layer.
4. Each individual Neuron present in the layer is called weight.
5. In addition to this, these neurons are connected to every other neuron in the adjacent layers.

Note: The information flow of the Neural network moves from left to right from Input to the output layer.

Benefits Data Scientist Can Get Using Neural Network

• As we know, the Neural network came from, and it’s an upgrade to AI, so instead of using AI to solve the large clusters of data, you can take the help of the neural network, making the process faster, reliable, and efficient.
• The network can learn things by themselves and come out with the output, which is not limited to the Input you have provided them in the first place.
• Once you input your data, it gets stored in the neural network means you don’t have to provide extra storage space.
• As a result of no storage requirement, you don’t have to worry about your data being lost or neural network not being fed with the regular data.
• Also, the neural network learns from its previous task, so if you provide the data and ask for a particular pattern to find in the data, it will find out the exact pattern in much less time as compared to its first time of processing.
• If in case you might have missed some value or you entered a wrong value in the algorithm, you don’t have to start all over again as it can detect the fault by itself and compensate on its own from the previous encounter to give you an output. Thus, you can expect negligible to no error while applying neural networks for analyzing your data.
• The neural network being a nonlinear model is relatively easy to implement in comparison to statistical methods. Likewise, it’s non-parametric character doesn’t require individuals to have a vast knowledge of statistics.
• Lastly, with the help of these networks, you can be sure there won’t be any effect on the performance of your system as neural networks provide optimal support to multiple tasking.

What Future of Neural Network Holds for Data Science?

A neural network is expensive at this moment, and to use it on large data sets requires a lot of capital. But in the upcoming years, it’s undoubtedly about to change with the time an innovation starts integrating with other technology to increase the efficiency and lower the cost.

Also, data is not going anywhere, and you are not going to dispose of your data; no one can. Big companies have massive data sets stored in hundreds of Terabytes, and it keeps on multiplying daily, As a result, to find out the useful data and use it according to the research requirement neural network is essential to save time as well money. 

Neural networks and data science complement each other. With the help of one, you can quickly get the most onerous task done, which has taken a lot of your time. On the other hand, data science helps the neural networks to outshine and perform at its full potential.

While the neural network is still a new technology its increasing flexibility can be used in a lot of ways, and it’s fair to say its a future technology and everyone should be looking at it with the eyes of curiosity.

Author Bio

Great Learning is an online and hybrid learning company that offers high-quality, impactful and

industry-relevant learning programs catering to the needs of working professionals in a data-

driven world.