Case Study: Industry Use-cases of Neural Networks
Neural Network is a part of Artificial Intelligence, which uses a similar way for training the machine and predict some output as our mind learns new things and does prediction. In this article, we are going to discuss what is neural networks and their industry use cases.
What is Neural Network…?
A neural network is a series of algorithms that help to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. Neural networks have a vast variety of applications: natural language processes, image recognition, and video recognition, recommender systems, etc. They are also applied in agriculture, to process images from satellites and predict weather conditions.
Neural networks are used to find the relationship between a large amount of data where we don’t perform data preprocessing manually such as on images.
Use of Neural Network
Neural network can be applied to real-world problems in many ways. In fact, they have already been implemented in many industries.
Let’s look at some examples of neural network applications in different area:
- eCommerce — for personalizing the purchaser’s experience.
- Finance — for fraud detection management and forecasting.
- Healthcare — to examine patients and diagnosing.
- Security — to avoid computer viruses, creating some antivirus that auto-detect the virus(program) according to their behavior in system.
We will most probably use a Neural network when we have so much data with us, and accuracy matters the most to us, such as cancer detection.
Case Study: YELP
Yelp, is a crowd-sourced local business review and social networking site. The site has pages devoted to individual locations, such as restaurants or schools, where Yelp users can submit a review of their products or services using a one to five star rating system.
The company is using artificial intelligence to better serve its millions of users. Yelp is enhancing the consumer the customer services and experiences by letting users post reviews along with images attached to them. Use of picture classification technology by the company has allowed companies to compile, manage, label and categories the images which are a reflection of the data of the reviewers and their reviews.
They say that they use deep learning which helps them most to identify fake and real reviews. In Yelp’s case, its software is using image analysis techniques to identify color, texture, and shape, meaning it can recognize the presence of say, burritos, or whether a restaurant has outdoor seating.
At this point, “the company is now able to predict attributes like ‘good for kids’ and ‘ambiance is classy’ with 83% accuracy” based solely on photos (arguably a more reliable source of information than user-submitted reviews, which can often be terribly biased or just factually incorrect). Soon, Yelp will be able to use this information “to auto-caption images, improve search recommendations, and better select an assortment of images to feature on businesses’ listing pages.”
So AI and neural networks can be applied to a vast number of use cases.
Thanks for reading :)
