Neural Networks
What is Neural Networks?
Neural Networks is a series of algorithms that enables us to recognize the relationships between the models or the sets of the data that imitates the way as like the human brain works. Also, neural networks refer to the system of neurons, either organic or artificial in nature.
We can also say that, Neural Network is somehow a collection of various nodes or the units which are called as neurons. These neurons can also connect to each other by transmitting the signal to the other neuron. First of all, neuron receives a signal then it transmits this signal to other neurons, similarly, the transmission kept on working until we receive the output from each of the neurons, after computing some non-linear function of the sum of the inputs we can say that we got a signal output.
Types of Neural Networks
There are three major types of neural networks in deep learning that forms the basis of most of the pre-trained models:
— Artificial Neural Network (ANN) — An artificial neural network(ANN) is the piece of a computing system designed to work similarly like the way as human brain analyzes and processes information. It is the foundation of artificial intelligence (AI) and solves problems that would be impossible or difficult for human mind statistics solving ability.
— Convolution Neural Network (CNN) — It is a class of deep learning, most commonly applied in analyzing the visuals of the images or we can say that these are designed to process pixel data. CNN follows a hierarchical model which works on building a network, like a funnel and finally gives out a fully connected layer, where all the neurons are connected to each other and the output is processed.
— Recurrent Neural Network (RNN) — A recurrent neural network (RNN) is a class of artificial neural network where connections between the nodes form a directed graph along a given sequence and also make use of patterns to predict any given scenario. It is used in speech recognition and natural language processing.
Advantages of Neural Networks
- Ability to work with insufficient knowledge
- Cost and time beneficial
- Accurate in results
- Gives growth in job
- Good failing tolerance
- Stores information on the entire network
- Moderate corruption
- Ability to train the machine
- Can do parallel processing
- Distributed memory
Use Cases solved by Neural Networks
How GovZilla uses Neural Networks?
Govzilla is a California-based company that provides a platform that supports AI and BigData to make government data more accessible and usable for the organizations for inspection, regulation, and enforcement of required compliances. Folio3 AI also developed a user portal for enables to use this portal for getting the forms, inspection and FDA letters while tracking their entire inspection process on the top of the platform.
Challenge: Govzilla required a complete user portal to enable users to easily search for government documents and forms, receive alerts, and greatly reduce research time.
Solution: Folio3 developed a completed portal that enabled GovZilla to continuously monitor and collect data from multiple type of agencies and tag data to enable reports while allowing their users to make user-friendly searches, automate the alerts to reduce regulatory risks, maximize actionable insights, and minimize research time.
With the new portal Govzilla users were able to easily access government regulation forms and compliance data and documents. In addition to tagging key areas of this data, users could automate the entire process of creating cases and distributing compliance notices leading to increased efficiency and speed.
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