Analytics Vidhya
Published in

Analytics Vidhya

Neural Networks in Everyday life

What actually is a neural network ???

A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. This means that neural networks can be able to do learn and process the data in the same way that the humans do. So there is need of huge data to help these neural networks to learn things and loads of resources to compute them internally. They can adapt to changing input; so the network generates the best possible result without needing to redesign the output criteria. These layers contain a lot of internal layers that are designed to process the data. The more the deep layers , the efficient is the output. These layers are fed with data in two ways , one to process the input data and the other to increase the efficiency of the output data that acts as input for the next layer.

When to use a neural network ???

You will most probably use a Neural network when you have so much data with you(and computational power of course), and accuracy matters the most to you. For Example, Cancer Detection. You cannot mess around with accuracy here if you want this to be used in actual medical applications. Hence it is also important to know when to use a neural network and the effect of efficiency in the use case.

Neural Network in Everyday life !!!

In everyday routine , neural networks are taking over most of the works right from simple object detection to advance auto pilot in flights. The way these neural network learn and predict the outcome is the most efficient and sometimes it also outsmarts the humans in most of the activities. The most important advantage is that these neural networks are implemented on the computers. So they are never lazy or bored to perform a prediction , so they can be the fastest and most efficient.

“We’re just seeing the beginning of neural network/AI applications changing the way our world works.”

The neural networks mostly find their use cases in solving various business management techniques. Real-world business applications for neural networks are booming. In some cases, NNs have already become the method of choice for businesses that use analytics, marketing and fraud detection. Neural nets and AI have incredible scope, and you can use them to aid human decisions in any sector. The field of neural networks and its use of big data may be complex and high-tech, but its best way to increase the efficiency and it’s ultimate purpose is to serve people.

Neural Networks in Data Mining:

Data mining refers to the use of only the required fields of data from a vast data collected. The neural network helps us to even predict the fields of data that is more effecting our outcome. So it can enhance our decision making with the available data.

“In more practical terms, neural networks are non-linear statistical data modeling tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data. Using neural networks as a tool, data warehousing firms are harvesting information from datasets in the process known as data mining.”

Artificial Neural Networks (ANN) capabilities fall within very broad categories, and some deep learning methods have achieved human-competitive performance on certain tasks. They find their use case in ;

  • pattern recognition (radar systems, face identification, signal classification, object recognition, etc.)
  • system identification and control (e.g., vehicle control, trajectory prediction, process control, natural resource management)
  • quantum chemistry
  • playing board and video games and decision making
  • sequence recognition (such as gesture, speech, handwritten and printed text recognition)
  • medical diagnosis
  • directing manipulators and prostheses
  • finance (e.g., automated trading systems)
  • data mining
  • visualization
  • machine translation
  • social network filtering
  • black-box models in geoscience (hydrology, ocean modeling and coastal engineering, and geomorphology)

… and many more.

Companies using Neural Networks : Lot of companies are heading towards the implementation of these NNs in their services to attract customers to use their services. Some of those companies are listed below ;

ContractProbe: It’s an automator of contract reviews. The online proofreading tool is designed for attorneys, notaries, and other professionals who deal with multiple legal documents daily. The user uploads a document in PDF, Word or plain text file, and in less than a minute is able to read a summary report.

The mechanism is based on ANNs that recognize patterns in the uploaded texts. They have been trained on thousands of executed non-disclosure agreements, intellectual property licenses, independent contractor agreements, employment contracts, and other types of agreements. In addition, the artificial intelligence ‘front-end’ is further learning from each new document that ContractProbe processes.

Twitter — Curated Timelines

Twitter has been at the center of numerous controversies of late (not least of which were the much-derided decisions to round out everyone’s avatars and changes to the way people are tagged in @ replies), but one of the more contentious changes we’ve seen on Twitter was the move toward an algorithmic feed. Twitter’s AI evaluates each tweet in real time and “scores” them according to various metrics.

Ultimately, Twitter’s algorithms then display tweets that are likely to drive the most engagement. This is determined on an individual basis; Twitter’s machine learning tech makes those decisions based on your individual preferences, resulting in the algorithmically curated feeds, which kinda suck if we’re being completely honest.

IWatch of Apple

The watch launched by apple is the most advanced watch in the history of watches which makes sure that you manage your daily activities , take a complete health check including blood-oxygen level , automatic sleep tracking , fall detection , ECG scan and many more just from your watch.

It even uses advanced algorithms that are trained on Neural Networks which track every workout that you do and apply analytics on the data and finally prefer you to take necessary actions. The NNs play a vital role in these type of use cases and there is also a need to make sure that their efficiency is at it’s peaks so that they don’t recommend something wrong to the consumer.

Voice assistants :

Another big application of NNs is the voice assistants. The voice assistants in everyday life function on the trained neural networks to assist and give you the results that you asked for.

They are trained to detect various slangs , accents and different meanings that they mean even when they speak the same sentence. They have been trained to maximum efficiency to make sure that the right required output is delivered.

Implementation of NNs in business have seen a drastic up-rise in development of the businesses of many companies and finally benefitted the people using them.

--

--

--

Analytics Vidhya is a community of Analytics and Data Science professionals. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com

Recommended from Medium

Why Does Deep Learning Work?

Why you really won’t get fired buying from IBM

AI in China: Technology for development?

Technological redlining: how algorithms are dividing the country

Google’s algorithm labelled an African American person as “gorilla”

Gender Bias in AI Systems

Free speech — Linguistic bias in NLP

What’s good and what’s not about big data and/or AI?

Top 5 chatbots for workflow management and process automation

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Yashwanth Medisetti

Yashwanth Medisetti

More from Medium

Classification And Regression Tree (CART)

Solving a Supervised Learning Problem — Logistic Regression: Predict a Passenger Survival

Deciding Variables in Multiple Linear Regression

Linear Classification in Machine Learning