INSUBCONTINENT EXCLUSIVE:
A neural network is a type of machine learning which models itself after the human brain
This creates an artificial neural network that via an algorithm allows the computer to learn by incorporating new data.While there are
plenty of artificial intelligence algorithms these days, neural networks are able to perform what has been termed deep learning
While the basic unit of the brain is the neuron, the essential building block of an artificial neural network is a perceptron which
accomplishes simple signal processing, and these are then connected into a large mesh network.The computer with the neural network is taught
to do a task by having it analyze training examples, which have been previously labeled in advance
A common example of a task for a neural network using deep learning is an object recognition task, where the neural network is presented
with a large number of objects of a certain type, such as a cat, or a street sign, and the computer, by analyzing the recurring patterns in
the presented images, learns to categorize new images.How neural networks learnUnlike other algorithms, neural networks with their deep
learning cannot be programmed directly for the task
The learning strategies go by three methods:Supervised learning: This learning strategy is the simplest, as there is a labeled dataset,
which the computer goes through, and the algorithm gets modified until it can process the dataset to get the desired result.Unsupervised
learning: This strategy gets used in cases where there is no labeled dataset available to learn from
The neural network analyzes the dataset, and then a cost function then tells the neural network how far off of target it was
The neural network then adjusts to increase accuracy of the algorithm.Reinforced learning: In this algorithm, the neural network is
reinforced for positive results, and punished for a negative result, forcing the neural network to learn over time.History of neural
networksWhile neural networks certainly represent powerful modern computer technology, the idea goes back to 1943, with two researchers at
neuron is the basic unit of brain activity
However, this paper had more to do with the development of cognitive theories at the time, and the two researchers moved to MIT in 1952 to
including the Perceptron which accomplished visual pattern recognition based on the compound eye of a fly
In 1959, two Stanford University researchers developed MADALINE (Multiple ADAptive LINear Elements), with a neural network going beyond the
theoretical and taking on an actual problem
MADALINE was specifically applied to decrease the amount of echo on a telephone line, to enhance voice quality, and was so successful, it
remains in commercial use to current times.Despite initial enthusiasm in artificial neural networks, a noteworthy book in 1969 out of MIT,
Perceptrons: An Introduction to Computational Geometry tempered this
The authors expressed their skepticism of artificial neural networks, and how this was likely a dead end in the quest for true artificial
Despite this, some efforts did continue, and in 1975 the first multi-layered network was developed, paving the way for further development
in neural networks when John Hopfield, a professor at Princeton University, invented the associative neural network; the innovation was that
data could travel bidirectionally as previously it was only unidirectional, and is also known for its inventor as a Hopfield Network
Going forward, artificial neural networks have enjoyed wide popularity and growth.Real world uses for neural networksHandwriting recognition
is an example of a real world problem that can be approached via an artificial neural network
is unique, with different styles, and even different spacing between letters, making it difficult to recognize consistently.For example, the
first letter, a capital A, can be described as three straight lines where two meet at a peak at the top, and the third is across the other
network approach, the computer is fed training examples of known handwritten characters, that have been previously labeled as to which
letter or number they correspond to, and via the algorithm the computer then learns to recognize each character, and as the data set of
characters is increased, so does the accuracy
Handwriting recognition has various applications, as varied as automated address reading on letters at the postal service, reducing bank
fraud on checks, to character input for pen based computing.Another type of problem for an artificial neural network is the forecasting of
interest rates and various currencies
In the case of the stock market, traders use neural network algorithms to find undervalued stocks, improve existing stock models, and to use
the deep learning aspects to optimize their algorithm as the market changes
There are now companies that specialize in neural network stock trading algorithms, for example, MJ Trading Systems.Artificial neural
network algorithms, with their inherent flexibility, continue to be applied for complex pattern recognition, and prediction problems
In addition to the examples above, this includes such varied applications as facial recognition on social media images, cancer detection for
medical imaging, and business forecasting.zSiqDbRESd2rmwZis83Rvh.jpg#
