Neural Networks: The Computational Brain
One of the key parts of cutting edge AI technology, Artificial Neural Networks (ANNs) are becoming too important and common place to ignore. However, Artificial Neural Networks and the role that they play can be a difficult concept to understand.
In this article, we’ll explain exactly what Artificial Neural Network is and how they work.
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.
How Do 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.
Educating Artificial Neural Networks:
For artificial neural networks to learn they require a mass of information.
This information is known as a training set.
If you wanted to teach your ANN to learn how to recognise a cat your training set would consist of thousands of images of a cat.
These images would all be tagged “cat”.
Once this information has been inputted and analysed the network is considered trained.
From now on it will try to classify any future data based on what it thinks it is seeing.
So if you present it with a new image of a cat, it will identify the creature.
As a check, during the training period, the system’s output is matched against the description of the data it’s analysing.
If the information is the same, the learning process is validated.
If the information is different backpropagation is used to adjust the learning process.
Backpropagation involves working back through the layers, adjusting the set mathematical equations and parameters.
These adjustments are made until the output data presents the desired result.
This process, deep learning, is what makes the network adaptive.
The network is able to learn and adapt as more information is processed.
Types of Neural Networks : -
There are many types of neural networks available or that might be in the development stage. They can be classified depending on their: Structure, Data flow, Neurons used and their density, Layers and their depth activation filters etc.
1) Recurrent Neural Network (RNN)
In this network, the output of a layer is saved and transferred back to the input. This way, the nodes of a particular layer remember some information about the past steps. The combination of the input layer is the product of the sum of weights and features. The recurrent neural network process begins in the hidden layers.
2) Convolutional Neural Network (CNN)
This network consists of one or multiple convolutional layers. The convolutional layer present in this network applies a convolutional function on the input before transferring it to the next layer. Due to this, the network has fewer parameters, but it becomes more profound. CNNs are widely used in natural language processing and image recognition.
3) Feedforward Neural Network (FNN)
This is the purest form of an artificial neural network. In this network, data moves in one direction, i.e., from the input layer to the output layer. In this network, the output layer receives the sum of the products of the inputs and their weights. There’s no back-propagation in this neural network. These networks could have many or zero hidden layers. These are easier to maintain and find application in face recognition.
Tasks Which Neural Networks Can Perform : -
Neural networks are highly valuable because they can carry out tasks to make sense of data while retaining all their other attributes. Here are the critical tasks that neural networks perform:
- Classification: NNs organize patterns or datasets into predefined classes.
- Prediction: They produce the expected output from given input.
- Clustering: They identify a unique feature of the data and classify it without any knowledge of prior data.
- Associating: You can train neural networks to “remember” patterns. When you show an unfamiliar version of a pattern, the network associates it with the most comparable version in its memory and reverts to the latter.
- These include analyzing data, transcribing speech into text, powering facial recognition software, or predicting the weather.
Some Real World Applications are:
- 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.
This application of Artificial Neural Networks can save businesses both time and money.
2. Reducing Email Fatigue and Improving Conversion Rates:
By only advertising relevant products to interested customers, you also reduce the chances of customers developing email fatigue.
In short, if your advertisements are relevant and interesting customers are more likely to interact.
This drives visits to your website, potentially increasing sales, and helps you to build a strong client-business relationship.
3. Improving Search Engine Functionality:
During 2015 Google I/O keynote address in San Francisco, Google revealed they were working on improving their search engine.
These improvements are powered by a 30 layer deep Artificial Neural Network.
This depth of layers, Google believes, allows the search engine to process complicated searches such as shapes and colours.
Using an Artificial Neural Network allows the system to constantly learn and improve.
This allows Google to constantly improve its search engine.
4. 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.
5. Keeping Customers Loyal to Your Company:
Artificial Neural Networks can also identify customers likely to switch to a competitor.
By knowing which customers are most likely to defect you are able to target them with tailored marketing campaigns.
Offering incentives, or friendly reminders about your company, will encourage customers to stick around.
This predictive use of Artificial Neural Networks is already benefiting FedEx.
6. Artificial Neural Networks in Financial Services:
When it comes to AI banking and finance, Artificial Neural Networks are well suited to forecasting.
This suitability largely comes from their ability to quickly and accurately analyse large amounts of data.
Artificial Neural Networks are capable of processing and interpreting both structured and unstructured data.
After processing this information Artificial Neural Networks are also able to make accurate predictions.
7. Artificial Neural Networks are Revolutionising Business Practises
Artificial Neural Networks may be a complex concept to fully understand.
However, by using them in conjunction with deep learning tools allows computer-driven technology to make gigantic leaps forward.
From streamlining manufacturing to product suggestions and facial scanning, Artificial Neural Networks are transforming the way businesses operate.
CONCLUSION
Neural computers perform very favorably in business and military applications. They do not require explicit programming by an expert and are robust to noisy, imprecise or incomplete data.
You cannot afford to ignore the fact that your competitors are already investigating the opportunities and realizing the significant business benefits that neural technology brings to a range of applications.
