Industry use cases of Neural Networks
What are Neural networks?
Neural networks are a series of algorithms that mimic the operations of a human brain to recognize relationships between vast amounts of data. They are used in a variety of applications in financial services, from forecasting and marketing research to fraud detection and risk assessment.
How a Neural Network Works?
A neural network has many layers. Each layer performs a specific function, and the complex the network is, the more the layers are. That’s why a neural network is also called a multi-layer perceptron.
The purest form of a neural network has three layers:
- The input layer
- The hidden layer
- The output layer
Use Cases of Neural Networks
Artificial Neural Networks can be used in a number of ways. They can classify information, cluster data, or predict outcomes. ANN’s can be used for a range of tasks. These include analyzing data, transcribing speech into text, powering facial recognition software, or predicting the weather.
Top Companies using Artificial Neural Network(ANN)
- Nvidia Corp. (NVDA)
- Alphabet (GOOG, GOOGL)
- Salesforce.com (CRM)
- Amazon.com (AMZN)
- Microsoft Corp. (MSFT)
- Twilio (TWLO)
- IBM (IBM)
- Facebook (FB)
Case Study: Netflix
Netflix Inc. isn’t going to leave the success of its series and films to chance — and analysts say its stock should be rewarded.
The company wants to be able to “combine great story telling and the great technological aspects,” Chief Executive Reed Hastings told MarketWatch in 2015. “That’s where we want to be.”
Netflix’s use of convolutional neural network and proprietary algorithms, which is essentially deep machine learning used to analyze visual imagery, is a prime example of its approach.
And it’s just that approach that grabbed the attention of Wells Fargo analysts Ken Sena and Marci Ryvicker. They initiated coverage of Netflix NFLX, +2.19% on Wednesday with an overweight rating and a $230 12-month price target, which is the highest price target among analysts covering the stock, according to FactSet.
Sena and Ryvicker said improvements and advancements in neural networks allow Netflix to effectively push recommended shows and movies to subscribers and even use data to make decisions in the production and acquisition of content.
Brian David Johnson, futurist in residence at Arizona State University, said people have been able to analyze video on a frame-by-frame and pixel-by-pixel basis for years — he even wrote a book about it in 2009. But there were roadblocks in content licensing that made innovation tough, until now.
“Netflix has been working solidly to come up with algorithms to match consumers with their content — also Netflix has a lot more power than they did back in 2009 to get people to allow them to search their video,” Johnson said in an email to MarketWatch. “The advances in [artificial intelligence] and neural networks means that they can now make sense of that data.
“Essentially, they are looking for patterns in the data that equal the right output they are looking for. The scope and scale of AI allows them to do this in an unprecedented fashion.”
In addition to using the collected data for platform improvements such as adding auto play, a “skip intro” button, customized trailers and changing its stars rating system to thumbs up or down, Ryvicker and Sena said Netflix could use the capability for decision-making concerning content supply.
Ryvicker and Sena wrote that through advancements in convolutional neural networks, Netflix can detect and analyze underlying scene elements that drive viewer engagement. That data-driven approach, they wrote, can inform what content Netflix licenses and provide insight into production.
“We see this as an important factor driving Netflix’s achievement around originals, with renewal rates roughly three times that of what the traditional TV networks have produced” Ryvicker and Sena wrote, also noting Netflix’s 91 Emmy nominations — second-most behind HBO. “In addition, Netflix itself estimates that these efficiencies, combined with higher subscriber retention, saves the company over $1 billion each year.”