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.
Then let see how ANN works..
👉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.
This response is based on the information it has learned throughout the process.
👉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.
👉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.
These include analyzing data, transcribing speech into text, powering facial recognition software, or predicting the weather.
👉Different Types of Neural Networks
There are many types of 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.
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.
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 here are some example where Artificial Neural Network are currently being applied.
👉 ANN’s Use Cases
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.
Within a few months, Google was already noticing improvements in search results.
The company reported that its error rate had dropped from 23% down to just 8%.
Google’s application shows that neural networks can help to improve search engine functionality.
Similar Artificial Neural Networks can be applied to the search feature on many e-commerce websites.
This means that many companies can improve their website search engine functionality.
This allows customers with only a vague idea of what they want to easily find the perfect item.
Amazon has reported sales increases of 29% following improvements to its recommendation systems.
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.
Forbes reports that FedEx can predict which customers are likely to leave with an accuracy of 60-90%.
By applying Artificial Neural Networks in this way we can enhance and personalise the consumer’s experience.
Encouraging repeat custom and helping to build a relationship between your business and your customers.
DeepFace is a form of facial recognition software-driven by Artificial Neural Networks.
It is capable of mapping 3D facial features.
Once the mapping is complete the software turns the information into a flat model.
The information is then filtered, highlighting distinctive facial elements.
To be able to do this DeepFace implements 120 million parameters.
This technology hasn’t just emerged overnight.
DeepFace has been trained with a pool of 4.4 million tagged faces.
During the training process, tests were carried out presenting the system with side-by-side images.
The system was then asked to identify if the images are of the same person.
In these tests, DeepFace returned an accuracy rating of 97.25%.
Human participants taking the same test scored, on average, 97.5%.
Facebook has also taken its software to computing and technology conferences.
This is done with the purpose of allowing academics and researchers to assess and inspect the technology.
With all this work it’s little wonder that DeepFace may be the most accurate facial technology software yet developed.