Deep Neural Network Examples from Real-life

deep neural network examples from real-life

The deep neural network (DNN) is an artificial neural network, which has a number of hidden layers and nodes. Deep NN is composed of many interconnected and non-linear processing units that work in parallel to process information more quickly than the traditional neural networks. Deep learning algorithms are used for classification, regression analysis, prediction and other types of tasks. In this blog post, we will present deep neural network examples from the real-world/real-life.

Before jumping into examples, you may want to check out some of my following posts on deep neural network:

Also, lets understand some terminologies which will later used in the examples.

What are different types of deep neural networks?

The following are different types of deep neural networks:

  • Standard artificial neural network (ANN): Standard deep ANN are neural networks with multiple hidden layers. The regression and classification problems dealing with large number of variables can be solved using Deep ANN.
  • Convolutional neural network (CNN): CNN is defined as a deep neural network having a set of different filters that is applied across the input to create an activation map. The problems that deal with images and videos can be solved using CNN.
  • Recurrent neural network (RNN): RNN is a deep neural networks that has the ability to store information from previous computations and passes it forward so as to work upon this data in a sequential manner. RNNs allow information to enter at certain points and flow in a specific direction. The problems which deal with time series data or sequence prediction tasks fall under the category of RNNs.
  • Long short-term memory (LSTM): LSTMs are deep neural networks, which have a mechanism to store the information for long periods. This allows it to learn from experiences that span over many time steps. The problems that deal with information from sequential data can be solved using LSTMs. LSTM are a type of Deep Recurrent Neural Network.
  • Stacked Autoencoders: Stacked autoencoders are deep neural networks that can be used to solve unsupervised tasks. Autoencoders are complex neural networks, which learn the representation of input data by finding a compressed version of this information through an unsupervised learning technique called Auto-encoders. The problems which deal with the generation of new data from input dataset can be solved using stacked autoencoders.
  • Variational autoencoders: Variational autoencoders are deep neural networks that can be used to solve both supervised and unsupervised tasks. The problems that deal with latent representation and Clustering fall under the category of Variational Deep Learning. The difference between variational autoencoder and stacked autoencoders is that stacked autoencoders learn a compressed version of input data while variational autoencoders learns a probability distribution.

Here is a quick diagram representing the most common type of neural networks including ANN, CNN and RNN.

deep neural network examples from real-life

Real world examples of Deep Neural Networks

The following are some of the examples of real world applications built using different types of deep neural networks such as those listed above:

  • Housing price prediction: Standard artificial neural network (ANN) can be used for the real estate market. Deep learning approach can also be used to predict the housing prices in a given area, city or country with high accuracy and low risk involved. The input data can be different home features and the output prediction will be pricing estimate. This is a supervised learning problem.
  • Whether user will click on advertisement: Standard ANN can be used to predict whether a user will click on an ad or not. The input data is advertisement and user information and the output can be label such as click (1) or not click (0). This is a supervised learning problem.
  • Weather prediction: Recurrent neural network (RNN) or LSTM can be used for predicting weather as the data is temporal or sequential and time-series based. As a matter of fact, a custom or hybrid model built using temporal based network algorithms (RNN / LSTM) and CNN can be used.
  • Image classification: Real world applications of image classification includes classification of images with humans, objects and scenes. Business applications for image classification include surveillance, medical diagnosis (healthcare), tagging of images, X-ray interpretation, CT-Scans/MRI interpretation and so on. Deep neural networks have the capability to recognize images at a pixel level which is almost impossible for humans. During Covid-19 times, CNN models were used to classify X-ray/CT-scan images in predicting likelihood of a person suffering from Covid. The input data are different images and output represent different labels. The predictions represent probability that the input is same as one of the output labels. This is a supervised learning problem.
  • Machine translation: Deep neural networks can be used to translate languages by learning the semantic representation of words in one language and then mapping them into another language’s word-meaning representations. Recurrent Neural Networks (RNNs) can be used for machine translation problems, where information flows sequentially across time steps. Deep NLP is used for Deep Learning applications in Natural Language Processing (NLP), which is also sometimes referred to as Deep Linguistic Analysis. This is a supervised learning problem.
  • Speech recognition: Deep neural networks can be used to recognize speech. Deep learning models for Speech Recognition are Deep Neural Networks trained using Deep Learning techniques/algorithms, specifically Deep Feed-Forward NN (FFNN), Deep Recurrent NN (RNN) and LSTM. The input data can be audio and the output data will be text transcript. A key aspect of learning will comprise of supervised learning.
  • Face recognition: With the advent of Deep learning, Deep neural networks can be used for face recognition. Deep neural network models offer a viable solution to the problem of image classification and have gained many improvements in accuracy levels which was not possible with other machine learning algorithms/techniques so far. Deep neural networks such as Deep Convolutional Neural Network (DCNN) and Deep Belief Networks (DBN) can be used for face recognition problems in real-world applications. This can be primarily termed as supervised learning problem.
  • Autonomous driving: A custom or hybrid neural network architecture comprising of CNN, ANN etc will be required to build a bunch of models which can be used for autonomous driving. The input to these models will be images, radar information (device put on the top of the car) and the label / output will be position of other vehicles, objects etc which will help in deciding the way of safe driving. As like the above problem, a key aspect of learning will be supervised learning.

Deep neural networks are a powerful tool in the world of Deep Learning and Deep Linguistic Analysis. In this blog post, the examples from different industry verticals were provided. In these examples, different types of deep neural networks (such as ANN, CNN, RNN, LSTM etc) have been used successfully to solve difficult real-world or real-life problems. In case, you want to get trained in Deep Learning, Deep Neural Network or Deep Linguistic Analysis, please feel free to reach out.

Ajitesh Kumar
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Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking
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