Deep Neural Network (DNN), its scope, and nature of complexity

3 min readNov 30, 2021



In the current world of increasing obstacles and intricacies of business problems, there is a need for advanced computer appliances that can visualize things like the human mind and make decisions even in a better manner. In contrast to it, Machine Learning tools like DNN play a vital role in determining such obstacles and intricacies of business problems in a motorized and effective manner. It can deliver promising decisions and perform better than a human brain.

What is a DNN?

A Deep Neural Network (DNN) is a branch of machine learning and a standard mechanism to resolve computer vision problems. Furthermore, it is an advanced artificial neural network with multiple layers between the input and output layers. It works like neurons of the human brain and is beneficial to replace human labor with autonomous work. When it gets a new image in the system, it discovers how to act on the reported situations and resolve business problems based on the extracted features of the input image.

Deep Neural Network

Structure of DNN

The diagram shows the structure of DNN where you can see the connection between different layers composed of artificial neurons, connected to each other like a human brain. The input layers of DNN create electric impulses, which quickly travel through the hidden layers and then connect with the output layer.

The layered structure of DNN

Scope of DNN

DDN have great scope in the computer vision field and speech recognition. They enable machines to recognize images, speech, and play games with higher accuracy in the comparison of humans.

Training complexity of DNN

To achieve a higher level of accuracy, huge data and computing power are being demanded by DNN. Along with this, the training of DNN includes higher complexity in the comparison of ANN. Thus, while training of DNN in order to reduce training time and improve model accuracy, certain important guidelines are needed to be followed such as:

  1. The data pre-processing is important to rectify the missing values in the input data so that the neural network can achieve the desired level of accuracy. If data would not be pre-processed, then it may affect the accuracy as well as the performance of the network.
  2. Overfitting is the most common problem that is experienced while training deep neural networks. Thus, the learning algorithm needs to fit every data point in the input, even if there is a representation of sampled noise. Regularization helps to avoid the problem of overfitting by penalizing the weights of the network.


In conclusion, it has been investigated that DNN is an advanced ANN that is potent to resolve complex Computer Vision problems. However, the training of DNN to perform in an intended manner is more complex in the comparison of other types of ANN, for which data quality must be good, a structured approach of model training should be followed, and pre-processing of input data is needed.




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