Neural Network and Industry usecases of Neural Network ..!
Neural networks is one of the most powerful and widely used algorithms when it comes to the subfield of machine learning called deep learning. At first look, neural networks may seem a black box; an input layer gets the data into the “hidden layers” and after a magic trick we can see the information provided by the output layer. However, understanding what the hidden layers are doing is the key step to neural network implementation and optimization.
✨What are Neural Networks ?
A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature. Neural networks can adapt to changing input; so the network generates the best possible result without needing to redesign the output criteria.
Briefly, a neural network is defined as a computing system that consist of a number of simple but highly interconnected elements or nodes, called ‘neurons’, which are organized in layers which process information using dynamic state responses to external inputs. In the context of this structure, patterns are introduced to the neural network by the input layer that has one neuron for each component present in the input data and is communicated to one or more hidden layers present in the network; called ‘hidden’ only due to the fact that they do not constitute the input or output layer. It is in the hidden layers where all the processing actually happens through a system of connections characterized by weights and biases the input is received, the neuron calculate a weighted sum adding also the bias and according to the result and a pre-set activation function (most common one is sigmoid, σ, even though it almost not used anymore and there are better ones like ReLu), it decides whether it should be ‘fired’ or activated. Afterwards, the neuron transmit the information downstream to other connected neurons in a process called ‘forward pass’. At the end of this process, the last hidden layer is linked to the output layer which has one neuron for each possible desired output.
✨ Types of neural networks -
Neural networks can be classified into different types, which are used for different purposes. While this isn’t a comprehensive list of types, the below would be representative of the most common types of neural networks that you’ll come across for its common use cases
Perceptron :- The perceptron is the oldest neural network, created by Frank Rosenblatt in 1958. It has a single neuron and is the simplest form of a neural network
Feedforword neural Network :- Feedforward neural networks, or multi-layer perceptrons (MLPs), are what we’ve primarily been focusing on within this article. They are comprised of an input layer, a hidden layer or layers, and an output layer.
Convolutional neural networks (CNNs) are similar to feedforward networks, but they’re usually utilized for image recognition, pattern recognition, and/or computer vision.
Recurrent neural networks (RNNs) are identified by their feedback loops. These learning algorithms are primarily leveraged when using time-series data to make predictions about future outcomes, such as stock market predictions or sales forecasting.
✨ Industry usecases of Neural Networks
✅ Neural networks and IBM Cloud
For decades now, IBM has been a pioneer in the development of AI technologies and neural networks, highlighted by the development and evolution of IBM Watson. Watson is now a trusted solution for enterprises looking to apply advanced natural language processing and deep learning techniques to their systems using a proven tiered approach to AI adoption and implementation.
Watson uses the Apache Unstructured Information Management Architecture (UIMA) framework and IBM’s DeepQA software to make powerful deep learning capabilities available to applications. Utilizing tools like IBM Watson Studio and Watson Machine Learning, your enterprise can seamlessly bring your open-source AI projects into production while deploying and running your models on any cloud.
✅ Walmart using artificial neural networks to predict future product demand.
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.
✅Neural networks systems to Identify Credit Risks
HSBC is just one bank using Artificial Neural Networks to transform how loan and mortgage applications are processed.
The company uses neural networks to analyse customers with previous behaviour patterns.
This information can highlight personality traits that mark an applicant out as a credit risk.
Meanwhile, Natwest is developing a digital human chatbot called Cora.
Cora, currently, is only able to deal with simple requests.
However, as the technology develops it’s hoped that Cora will be able to help process mortgage and loan applications.
By applying Artificial Neural Networks, companies are able to provide a better service.
As well as reducing expense it means companies make fewer risky decisions, such as lending to credit risks.
This reduces potential losses and prevents people from running up debts they can’t afford.
✅The Amazing way how pharmaceutical industries are using neural networks.
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 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.
✨ Conclusion :-
The impact of AI on our lives will continoue to grow as more technology is integrated into everyday life. The main conclusion from the work was that neural networks are capable of improving industrial process monitoring and control systems.
