Let’s take a moment to consider the human brain. Made up of a network of neurons, the brain is a very complex structure.
Neural networks represent deep learning using artificial intelligence. Certain application scenarios are too heavy or out of scope for traditional machine learning algorithms to handle. As they are commonly known, Neural Network pitches in such scenarios and fills the gap.
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.
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 perceptron, 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.
So, A neural network is a system designed to act like a human brain. It’s pretty simple but prevalent in our day-to-day lives.
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
As the names suggest, each of these layers has a specific purpose. These layers are made up of nodes. There can be multiple hidden layers in a neural network according to the requirements. The input layer picks up the input signals and transfers them to the next layer. It gathers the data from the outside world.
The hidden layer performs all the back-end tasks of calculation. A network can even have zero hidden layers. However, a neural network has at least one hidden layer. The output layer transmits the final result of the hidden layer’s calculation.
Here’s how it works:
1. Information is fed into the input layer which transfers it to the hidden layer
2. The interconnections between the two layers assign weights to each input randomly
3. A bias added to every input after weights are multiplied with them individually
4. The weighted sum is transferred to the activation function
5. The activation function determines which nodes it should fire for feature extraction
6. The model applies an application function to the output layer to deliver the output
7. Weights are adjusted, and the output is back-propagated to minimize error
The model uses a cost function to reduce the error rate. You will have to change the weights with different training models.
The model compares the output with the original result
It repeats the process to improve accuracy
Different types of Neural Networks in Deep Learning
This article focuses on three important types of neural networks that form the basis for most pre-trained models in deep learning:
- Artificial Neural Networks (ANN)
- Convolution Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
Artificial Neural Network (ANN) — What is a ANN and why should you use it?
A single perceptron (or neuron) can be imagined as a Logistic Regression. Artificial Neural Network, or ANN, is a group of multiple perceptron's/ neurons at each layer. ANN is also known as a Feed-Forward Neural network because inputs are processed only in the forward direction:
ANN
As you can see here, ANN consists of 3 layers — Input, Hidden and Output. The input layer accepts the inputs, the hidden layer processes the inputs, and the output layer produces the result. Essentially, each layer tries to learn certain weights
Convolutional Neural Network
Applications on Convolution Neural Network
- Image processing
- Computer Vision
- Speech Recognition
- Machine translation
Convolution neural network contains a three-dimensional arrangement of neurons, instead of the standard two-dimensional array. The first layer is called a convolutional layer. Each neuron in the convolutional layer only processes the information from a small part of the visual field. Input features are taken in batch-wise like a filter. The network understands the images in parts and can compute these operations multiple times to complete the full image processing. Processing involves conversion of the image from RGB or HSI scale to grey-scale. Furthering the changes in the pixel value will help to detect the edges and images can be classified into different categories.
Propagation is uni-directional where CNN contains one or more convolutional layers followed by pooling and bidirectional where the output of convolution layer goes to a fully connected neural network for classifying the images as shown in the above diagram. Filters are used to extract certain parts of the image. In MLP the inputs are multiplied with weights and fed to the activation function. Convolution uses RELU and MLP uses nonlinear activation function followed by SoftMax. Convolution neural networks show very effective results in image and video recognition, semantic parsing and paraphrase detection.
Recurrent Neural Networks
Applications of Recurrent Neural Networks
- Text processing like auto suggest, grammar checks, etc.
- Text to speech processing
- Image tagger
- Sentiment Analysis
- Translation
- Designed to save the output of a layer, Recurrent Neural Network is fed back to the input to help in predicting the outcome of the layer. The first layer is typically a feed forward neural network followed by recurrent neural network layer where some information it had in the previous time-step is remembered by a memory function. Forward propagation is implemented in this case. It stores information required for it’s future use. If the prediction is wrong, the learning rate is employed to make small changes. Hence, making it gradually increase towards making the right prediction during the backpropagation.
USE CASES
Amazing way google uses the concept of Neural network
Deep learning involves building artificial neural networks which attempt to mimic the way organic(living) brains sort and process information. The “deep” in deep learning signifies the use of many layers of neural networks all stacked on top of each other. This data processing configuration is known as a deep neural network, and its complexity means it is able to process data to a more thorough and refined degree than other AI technologies which have come before it.
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.
Pinterest — Improved Content Discovery
Whether you’re a hardcore pinner or have never used the site before, Pinterest occupies a curious place in the social media ecosystem. Since Pinterest’s primary function is to curate existing content, it makes sense that investing in technologies that can make this process more effective would be a priority — and that’s definitely the case at Pinterest.
In 2015, Pinterest acquired Kosei, a machine learning company that specialized in the commercial applications of machine learning tech (specifically, content discovery and recommendation algorithms).
Today, machine learning touches virtually every aspect of Pinterest’s business operations, from spam moderation and content discovery to advertising monetization and reducing churn of email newsletter subscribers. Pretty cool.
Baidu — The Future of Voice Search
Google isn’t the only search giant that’s branching out into machine learning. Chinese search engine Baidu is also investing heavily in the applications of AI.
A simplified five-step diagram illustrating the key stages of
a natural language processing system
One of the most interesting (and disconcerting) developments at Baidu’s R&D lab is what the company calls Deep Voice, a deep neural network that can generate entirely synthetic human voices that are very difficult to distinguish from genuine human speech. The network can “learn” the unique subtleties in the cadence, accent, pronunciation and pitch to create eerily accurate recreations of speakers’ voices.
Far from an idle experiment, Deep Voice 2 — the latest iteration of the Deep Voice technology — promises to have a lasting impact on natural language processing, the underlying technology behind voice search and voice pattern recognition systems. This could have major implications for voice search applications, as well as dozens of other potential uses, such as real-time translation and biometric security.
Amazon Forecast now uses Convolutional Neural Networks (CNNs)
Amazon Forecast uses machine learning to generate accurate demand forecasts, without requiring any prior ML experience for inventory planning, workforce planning, energy demand forecasting and cloud infrastructure usage forecasting. This technology has been developed from over 20 years of forecasting at Amazon.com. Amazon Forecast is a fully managed service, so there are no servers to provision, and no machine learning models to build, train, or deploy.
Amazon Forecast can now use Convolutional Neural Networks (CNNs) to train forecasting models up to 2X faster with up to 30% higher accuracy. CNN algorithms are a class of neural network-based machine learning (ML) algorithms that play a vital role in Amazon demand forecasting system and enable Amazon.com to predict demand for over 400 million products every day.
Developing Personalized Treatment Plans
A personalized treatment plan can be more effective than adopting a standardized 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 analyze 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 personalized treatments. Artificial Neural Networks and machine learning tools are able to quickly and accurately analyze and present data in a useful way. This ability makes it the perfect tool for this form of research and development.
Artificial Neural Networks are Revolutionizing Business Practices
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.
Facial Recognition Software
Technology companies have long been working toward developing reliable facial recognition software.
One company leading the way is Facebook.
For a number of years now they have been using the facial recognition technology to auto-tag uploaded photographs.
They have also developed Deface.
Deface
Deface 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 Deface implements 120 million parameters. This technology hasn’t just emerged overnight. Deface has been trained with a pool of 4.4 million tagged faces.
These images were taken from 4,000 different Facebook accounts.
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, Deface 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 Deface may be the most accurate facial technology software yet developed.
Salesforce — Intelligent CRMs
Salesforce is a titan of the tech world, with strong market share in the customer relationship management (CRM) space and the resources to match. Lead prediction and scoring are among the greatest challenges for even the savviest digital marketer, which is why Salesforce is betting big on its proprietary Einstein machine learning technology.
Fraud Detection Applications
As technology advances, and more importance is placed on online transactions, fraudsters are also becoming more sophisticated.
Luckily Artificial Neural Networks can help to keep us, and our finances, safe.
Deep learning and Artificial Neural Networks applications are powering systems capable of detecting all forms of financial fraud. For example, this application can identify unusual activity, such as transactions occurring outside the established time frame. Visa has used smart solutions to cut credit card fraud by two thirds. Their sophisticated anti-fraud detection systems are working towards biometric solutions.
However the company also analyses information such as payment method, time, location, item purchased, and the amount spent.
Even a small deviation from the norm in any of these categories can highlight a potential fraud case. Within seconds smart solutions allow Visa to look at over 500 data elements to determine if a transaction is suspicious. Similarly, it can be embarrassing when our card is declined by a retailer. Especially if our account is in credit.
MasterCard is employing solutions powered by Artificial Neural Networks to reduce the chances of this happening. Currently, MasterCard has halved the chances of these errors from occurring.
In this article, I have explained exactly what Neural Network is and how they work.
To illustrate their importance I’ve also showed you some examples of how Neural Networks are already transforming businesses.
Thank you!!
