Nature is the best engineer and developer, hence we often copy its principles and structures. And neural networks are one of such cases.
Neural Networks are the computer program that reflect the behavior of human brain. It recognizes patterns and solve common problems in the fields of AI, machine learning, and deep learning.
What do you mean by Neural Network?
It recognizes the pattern underneath data by a series of algorithms. It is made up of neurons just like human brain where many things are connected in various ways. Whatever a neural network learns is hard-coded and becomes permanent. Human brain can forget but neural networks cannot.
Artificial neural networks (ANNs) are comprised of a node layers, containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network.
First of all we will talk about one basic and oldest neural network algorithm called Perceptron. Perceptron is a single layer neural network. Perceptron is a linear classifier (binary). Also, it is used in supervised learning. It helps to classify the given input data. It follows the following simple steps :
- All the inputs are multiplied with their weights.
- Add all the multiplied values and lets call it the weighted sum.
- Apply it to the appropriate Activation function.
Ultimately, the goal is to minimize our cost function to ensure correctness of fit for any given observation. As the model adjusts its weights and bias, it uses the cost function and reinforcement learning to reach the point of convergence, or the local minimum. The process in which the algorithm adjusts its weights is through gradient descent, allowing the model to determine the direction to take to reduce errors (or minimize the cost function). With each training example, the parameters of the model adjust to gradually converge at the minimum.
A typical Neural Network contains a large number of artificial neurons called units arranged in a series of layers:
- Input layer — It contains those units which receive input from the outside world on which the network will learn, recognize about or otherwise process.
- Output layer — It contains units that respond to the information about how it’s learned any task.
- Hidden layer — These units are in between input and output layers. The job of the hidden layer is to transform the input into something that the output unit can use in some way.
Applications of Neural Network
- Voice recognition
- Process modeling and control
- Medical Diagnosis
- Financial Forecasting
- Fraud detection
- Portfolio Management
- Face recognition
- Target marketing
- Credit Evaluation
and much more…
Industry use cases
- eCommerce: It is used for recommendation engines that gives a personalized shopping experience to online shoppers. The system analyzes the characteristics of certain items and shows similar ones. In other cases, it defines and remembers the person’s preferences and shows the items meeting them. For instance, Amazon, Alibaba, Flipkart, eBay etc.
The eBay ShopBot functions as an AI assistant to help users easily find products of interest using natural language. Users can communicate with the bot via text, voice or using pictures taken with their smartphone of images related to a particular product.
2. Finance: There are neural network applications for fraud detection, management, and forecasting.
ZestFinance is the maker of the Zest Automated Machine Learning (ZAML) platform, an AI-powered underwriting solution that helps companies assess borrowers with little to no credit information or history. The platform utilizes thousands of data points and provides transparency that other underwriting systems cannot, which helps lenders better assess populations traditionally considered “at risk.” ZAML is an end-to-end platform that institutions can implement and scale quickly.
Utilized by top banks in the U.S., Shape Security curbs credit application fraud, credential stuffing, scraping and gift card cracking by pinpointing fake users. The company’s machine learning models are trained on billions of requests, allowing the software to effectively distinguish between real consumers and bots. Shape Security’s Blackfish network also uses AI-enabled bots to detect compromised login credentials, alerting both customers and companies to security breaches instantly.
3. Healthcare: Health care organizations are leveraging machine-learning techniques, such as artificial neural networks (ANN), to improve delivery of care at a reduced cost. Applications of ANN to diagnosis are well-known.
IBM Watson is the most powerful artificial intelligence in the world. It took 2 years to train the neural network for medical practice. Millions of pages of medical academic journals, medical records, and other documents were uploaded to the system for its learning. And now it can prompt the diagnosis and propose the best treatment pattern based on the patient’s complaints and anamnesis.
4. Security: Neural networks are widely used for protection from computer viruses, fraud, etc.
One of the examples is ICSP Neural from Symantec. It protects from cyber attacks by determining the bad USB devices containing viruses and exploiting zero-day vulnerabilities.
5. Logistics: Neural networks are used in this industry for routing and dispatching. It lets a user to plan routes and monitor them; customize shipping routes in real-time with the help of predictive features.
It results in productivity increase, decrease in the investment of resources in labor and increase of profitability due to the agility in the processes that can be related to the supply chain.
6. Vehicle building: Under the hood of self-driving vehicles are neural networks.
Despite the difficulties involved in the development of autonomous vehicles, several organizations are funneling vast amounts of resources to create a truly safe model. Waymo, the Google’s subsidiary focused on autonomous driving, began development in 2009. Since then the company has been collecting data from its self-driving cars located in 20 different cities in the United States.
Apple and Volkswagen are teaming up to create self-driving shuttles for their workers. Apple has been testing its fleet of 62 autonomous cars in California since April 2017. Honda is also relying on artificial intelligence to improve car safety.
As we can see, neural networks, AI and ML are the future of all the industries. These technologies help to make decisions, automate the working processes, prevent fraud, and do other important tasks. And they will continue developing.
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