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Neural Network Intelligence is the overview of the features that allow Neural Networks, which are key components of machine learning, to use data processing and computer programming to mimic/simulate the way in which a human brain functions.

This is essentially the foundation of Artificial Intelligence as we know it. Neural Networks are designed to be much more accurate and perform functions that would be highly difficult for a human brain itself to do. Through specific inputs and outputs, these networks are coded to carry out special tasks. From finance, to personal communication, to education, neural networks are key to today's technology sector. Each network contains small neuron nodes, that of which replicate a human brain. The nodes carry information to distinguished inputs and outputs. With the specified input, each neural network is to carry out tasks and give the desired output. Because of its broad functions, neural network usage is extremely common across many industries and jobs.

Needs
Neural Networks are widely used across many industries and sectors. By using Artificial Intelligence, programmers are utilizing features of neural networks for aspects of financial and investment decision-making. These networks have the intelligence to retrieve imprecise data that would be considered difficult for a human or computer to understand. Neural Networks detect trends in data, making it more feasible to register this data and process it at efficient rates. Being able to fix and adapt itself after learning faults, neural networks are arguably most used in the Artificial Intelligence area of study/work.

Advantages
An Artificial Neural Network is regarded as a higher performing and more powerful brain model, but in reality it has quite a few more advantages. Unlike other forms of machine learning, neural networks have the intelligence to mimic the processing characteristics of a human brain, but to an even higher extent. Through self-organization, neural networks can generate their own representation of specific information that is learned. Within the adaptive learning aspect, ANN’s are designed to have the capability to learn how to solve specified tasks based on given data. With fault tolerance, many neural networks have the ability to keep the lost data from network damages, increasing sustainability. Even if a number of neurons inside the neural network are unresponsive, the network can provide the same output, after detecting the fault.

Applications
Initially designed in 1943, the Neural Network has recently come to light and become more widely used by tech companies. [I can add a lot more information regarding the history behind neural networks.] Arguably the most widely used sector is from that of Artificial Intelligence. Some of the applications that make neural networks more preferable for such jobs include: image processing, language processing and translation, route detection, speech recognition, and forecasting. Due to features such as these, its speed, and efficiency, Neural Networks have become widely utilized for complex problem solving.

Types of Neural Networks
Based on the number of hidden layers contained, neural networks are placed into three separate categories. Feed-forward Neural Networks, the most basic type, has a particular use-case, having information travel in a unidirectional manner. The most widely used neural networks fall under the Recurrent Neural Networks category. With higher complexity and efficiency, this case has data flowing in from multiple directions.This is also where the most learning and output processing goes on, as opposed to the final category, Convolutional Neural Networks. These networks are the most popular today for one main reason: ability to efficiently perform face recognition. This specific category takes on inputs, and allows attributes to be encoded to inputs.