đź’ˇMind Understanding with Neural Networking
In today`s world One of the key parts of cutting edge AI technology, Artificial Neural Networks (ANNs) are becoming too important and commonplace to ignore.
However, Artificial Neural Networks and the role that they play can be a difficult concept to understand.
In this article, I’ll explain exactly what Artificial Neural Network is and how they work.
To illustrate their importance I’ll also show you some examples of how Artificial Neural Networks are already transforming businesses.
Suggestion For beginner → Read every word of every line for better understanding .
What are Neural Networks?
Neural networks are a set of algorithms, they are designed to mimic the human brain, that is designed to recognize patterns. They interpret data through a form of machine perception by labeling or clustering raw input data.
Let’s take a moment to consider the human brain.
Made up of a network of neurons, the brain is a very complex structure. It’s capable of quickly assessing and understanding the context of numerous different situations. Computers struggle to react to situations in a similar way. Artificial Neural Networks are a way of overcoming this limitation.
First developed in the 1940s Artificial Neural Networks attempt to simulate the way the brain operates.
Sometimes called perceptrons, an Artificial Neural Network is a hardware or software system. Some networks are a combination of the two.
Consisting of a network of layers this system is patterned to replicate the way the neurons in the brain operate.
The network comprises an input layer, where data is entered, and an output layer. The output layer is where processed information is presented.
Connecting the two is a hidden layer or layers.
The hidden layers consist of units that transform input data into useful information for the output layer to present.
In addition to replicating the human decision making progress Artificial Neural Networks allow computers to learn.
Their structure also allows ANN’s to reliably and quickly identify patterns that are too complex for humans to identify. Artificial Neural Networks also allow us to classify and cluster large amounts of data quickly.
Applications in Deep Learning and Artificial Intelligence
→ Artificial neural networks are a form of deep learning.
→ They are also one of the main tools used in machine learning.
→ Consequently ANN’s play an increasingly important role in the development of artificial intelligence.
→ The rise in importance of Artificial Neural Network’s is due to the development of “backpropagation”.
→ This technique allows the system’s hidden layers to become versatile.
→Adapting to situations where the outcome doesn’t match the one originally intended.
→The development of deep learning neural networks has also helped in the development of Artificial Neural Networks.
→ Deep learning neural networks are networks made up of multiple layers.
→This allows the system to become more versatile.
→ Different layers are able to analyse and extract different features.
→ This process allows the system to identify new data or images.
→ It also allows for unsupervised learning and more complex tasks to be undertaken.
How do Artificial Neural Networks Work?
As we have seen Artificial Neural Networks are made up of a number of different layers.
Each layer houses artificial neurons called units.
These artificial neurons allow the layers to process, categorize, and sort information.
Alongside the layers are processing nodes.
Each node has its own specific piece of knowledge.
This knowledge includes the rules that the system was originally programmed with.
It also includes any rules the system has learned for itself.
This makeup allows the network to learn and react to both structured and unstructured information and data sets.
Almost all artificial neural networks are fully connected throughout these layers.
Each connection is weighted.
The heavier the weight, or the higher the number, the greater the influence that the unit has on another unit.
The first layer is the input layer.
This takes on the information in various forms.
This information then progresses through the hidden layers where it is analysed and processed.
By processing data in this way, the network learns more and more about the information.
Eventually, the data reaches the end of the network, the output layer.
Here the network works out how to respond to the input data.
This response is based on the information it has learned throughout the process.
Here the processing nodes allow the information to be presented in a useful way.
What are Artificial Neural Networks Used for?
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.
There are many types of Artificial Neural Network.
Each has its own specific use.
Depending on the task it is required to process the ANN can be simple or very complex.
The most basic type of Artificial Neural Network is a feedforward neural network.
This is a basic system where information can travel in only one direction, from input to output.
Different Types of Neural Networks
The most commonly used type of Artificial Neural Network is the recurrent neural network.
In this system, data can flow in multiple directions.
As a result, these networks have greater learning ability.
Consequently, they are used to carry out complex tasks such as language recognition.
Other types of Artificial Neural Networks include convolutional neural networks, Hopfield networks, and Boltzmann machine networks.
Each network is capable of carrying out a specific task.
The data you want to enter, and the application you have in mind, affect which system you use.
Complex tasks such as voice recognition may require more than one type of ANN.
Now that we’ve established what Artificial Neural Networks are here are 10 examples of how they are currently being applied.
Artificial Neural Networks are Improving Marketing Strategies
By adopting Artificial Neural Networks businesses are able to optimise their marketing strategy.
Systems powered by Artificial Neural Networks all capable of processing masses of information.
This includes customers personal details, shopping patterns as well as any other information relevant to your business.
Once processed this information can be sorted and presented in a useful and accessible way. This is generally known as market segmentation.
To put it another way segmentation of customers allows businesses to target their marketing strategies.
Businesses can identify and target customers most likely to purchase a specific service or produce.
This focusing of marketing campaigns means that time and expense isn’t wasted advertising to customers who are unlikely to engage.
This application of Artificial Neural Networks can save businesses both time and money.
It can also help to increase profits.
The flexibility of Artificial Neural Networks means that their marketing applications can be implemented by most businesses.
Artificial Neural Networks can segment customers on multiple characteristics.
These characteristics can be as diverse as location, age, economic status, purchasing patterns and anything else relevant to your business.
One company making the most of this flexibility is cosmetics brand Sephora.
The email marketing campaign is tailored to the interests of each customer on the mailing list.
This allows them to offer a seamless, targeted marketing campaign.
This approach means that at a time when many companies are struggling Sephora is flourishing.
Developing Targeted Marketing Campaigns
Through unsupervised learning, Artificial Neural Networks are able to identify customers with a similar characteristic.
This allows businesses to group together customers with similarities, such as economic status or preferring vinyl records to downloaded music.
Supervised learning systems allow Artificial Neural Networks to set out a clear aim for your marketing strategy.
Like unsupervised systems, they can also segment customers into similar groupings.
However supervised learning systems are also able to match customer groupings to the products they are most likely to buy.
This application of technology can increase profits by driving sales.
Starbucks has used Artificial Neural Networks and targeted marketing to keep customers engaged with their app.
The company has integrated its rewards system location and purchase history on their app.
This allows them to offer an incredibly personalised experience, helping to increase revenue by $2.56 billion.
Applications of neural networks in the pharmaceutical industry
Artificial Neural Networks are being used by the pharmaceutical industry in a number of ways.
The most obvious application is in the field of disease identification and diagnosis.
It was reported in 2015 that in America 800 possible cancer treatments were in the trial.
With so much data being produced, Artificial Neural Networks are being used to help scientists efficiently analyse and interpret it.
The IBM Watson Genomics is one example of smart solutions being used to process large amounts of data.
IBM Watson Genomics is improving precision medicine by integrating genomic tumour sequencing with cognitive computing.
With a similar aim in mind, Google has developed DeepMind Health.
Working alongside a number of medical specialists such as Moorfields Eye Hospital, the company is looking to develop a cure for macular degeneration.
Developing Personalised Treatment Plans
A personalised treatment plan can be more effective than adopting a standardised approach.
Artificial Neural Networks and supervised learning tools are allowing healthcare professionals to predict how patients may react to treatments based on genetic information.
The IBM Watson Oncology is leading this approach.
It is able to analyse the medical history of a patient as well as their current state of health.
This information is processed and compared to treatment options, allowing physicians to select the most effective.
MIT’s Clinical Machine Learning Group is advancing precision medicine research with the use of neural networks and algorithms.
The aim is to allow medical professionals to get a better understanding of how disease forms and operates.
This information can help to design an effective treatment.
The team at MIT are currently working on possible treatment plans for sufferers of Type 2 Diabetes.
Meanwhile, the Knight Cancer Institute and Microsoft’s Project Hanover is using networks and machine learning tools to develop precision treatments.
In particular, they are focusing on treatments for Acute Myeloid Leukemia.
Vast amounts of information and data are required to progress precision medicine and personalised treatments.
Artificial Neural Networks and machine learning tools are able to quickly and accurately analyse and present data in a useful way.
This ability makes it the perfect tool for this form of research and development.
READ MORE Artificial Intelligence in Medicine -Top 10 Applications
READ MORE How Machine Learning Is Shaping Precision Medicine
Neural Networks in the Retail Sector
As we have noted, Artificial Neural Networks are versatile systems, capable of dealing reliably with a number of different factors.
This ability to handle a number of variables makes Artificial Neural Networks an ideal choice for the retail sector.
For instance, Artificial Neural Networks are, when given the right information, able to make accurate forecasts.
These forecasts are often more accurate than those made in the traditional manner, by analysing statistics.
This can allow accurate sales forecasts to be generated.
In turn, this information allows your businesses to purchase the right amount of stock.
This reduces the chances of selling out of certain items.
It also reduces the risk of valuable warehouse space being taken up by products you are unable to sell.
Online grocers Ocado are making the most of this technology.
Their smart warehouses rely on robots to do everything from stock management to fulfilling customer orders.
This information is used to power the trend of dynamic pricing.
Many companies, such as Amazon, use dynamic pricing to reproduced and increase revenue.
This application has spread beyond retail, service providers, such as Uber, even use this information to adjust prices depending on the customer.
Many retail organisations, such as Walmart, use Artificial Neural Networks to predict future product demand.
The network models analyse location, historical data sets, as well as weather forecasts, models and other pieces of relevant information.
This is used to predict an increase in sales of umbrellas or snow clearing products.
By predicting a potential rise in demand the company is able to increase stock in store.
This means that customers won’t leave empty-handed and also allows Walmart to offer product-related offers and incentives.
“Our first goal for these neural networks, or models, is to achieve human-level accuracy. Until you get to that level, you always know you can do better.”
Ivan Gomez ,Data Scientist and Consultant, Zencos
Neural networks have the ability to identify anomalies. In the future, we can use them to give doctors a second opinion — for example, if something is cancer, or what some unknown problem is. And we’ll be able to provide these second opinions faster and with more accuracy.
Leigh Ann Herhold , Data Scientist and Consultant , Zencos
🤩So guys it will give you some high level idea about Neural Networking concept !!
I tried to explain as much as possible. Hope You learned Something from here. Feel free to check out my LinkedIn profile and obviously feel free to comment and give feedback also .
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