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Task Description-
Research for industry use cases of Neural Networks and create a blog, Article or Video elaborating how it works.
In the past 10 years, the best-performing artificial-intelligence systems — such as the speech recognizers on smartphones or Google’s latest automatic translator — have resulted from a technique called “deep learning.”
Deep learning is in fact a new name for an approach to artificial intelligence called neural networks, which have been going in and out of fashion for more than 70 years. Neural networks were first proposed in 1944 by Warren McCullough and Walter Pitts.
What is Neural Network?
A branch of machine learning, neural networks (NN), also known as artificial neural networks (ANN), are computational models. 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. The concept of neural networks, which has its roots in artificial intelligence, is swiftly gaining popularity in the development of trading systems.
Basics of Neural Networks
A neural network works similarly to the human brain’s neural network. A “neuron” in a neural network is a mathematical function that collects and classifies information according to a specific architecture. The network bears a strong resemblance to statistical methods such as curve fitting and regression analysis.
A neural network contains layers of interconnected nodes. Each node is a perceptron and is similar to a multiple linear regression . The perceptron feeds the signal produced by a multiple linear regression into an activation function that may be nonlinear.
In a multi-layered perceptron (MLP), perceptrons are arranged in interconnected layers. The input layer collects input patterns. The output layer has classifications or output signals to which input patterns may map. Hidden layers fine-tune the input weightings until the neural network’s margin of error is minimal. The hidden layer is where artificial neurons take in a set of inputs based on synaptic weight, which is the amplitude or strength of a connection between nodes. These weighted inputs generate an output through a transfer function to the output layer.
Starting from the left, we have:
- The input layer of our model in orange.
- Our first hidden layer of neurons in blue.
- Our second hidden layer of neurons in magenta.
- The output layer (a.k.a. the prediction) of our model in green.
The arrows that connect the dots shows how all the neurons are interconnected and how data travels from the input layer all the way through to the output layer.
How the Biological Model of Neural Networks Functions?
What are neural networks emulating in human brain structure, and how does training work?
All mammalian brains consist of interconnected neurons that transmit electrochemical signals. Neurons have several components: the body, which includes a nucleus and dendrites; axons, which connect to other cells; and axon terminals or synapses, which transmit information or stimuli from one neuron to another. Combined, this unit carries out communication and integration functions in the nervous system. The human brain has a massive number of processing units (86 billion neurons) that enable the performance of highly complex functions.
How to Train Artificial Neural Networks (ANN)?
Single layer neural network (or perceptrons) can be trained using either the Perceptron training rule or the Adaline rule.
Perceptron Training Rule (Rosenblatt’s Rule):
A perceptron is a computational unit that calculates the output based on weighted input parameters.
Steps-
- Initialize the weights to 0 or small random numbers.
- For each training sample x(i): Compute the output value y ̂.
- Update the weights.
Adaptive Linear Neuron (Adaline) Rule (Widrow-Hoff Rule):
In Adaline, the weights are updated based on a linear activation function.
The linear activation function φ(z) is the identity function of the net input, so that:
φ(wTx) = wTx
While the linear activation function is used for learning the weights, a threshold function is used to make the final prediction, which is similar to the unit step function.
Attributes of Neural Networks
With the human-like ability to problem-solve — and apply that skill to huge datasets — neural networks possess the following powerful attributes:
- Adaptive Learning: Like humans, neural networks model non-linear and complex relationships and build on previous knowledge. For example, software uses adaptive learning to teach math and language arts.
- Self-Organization: The ability to cluster and classify vast amounts of data makes neural networks uniquely suited for organizing the complicated visual problems posed by medical image analysis.
- Real-Time Operation: Neural networks can (sometimes) provide real-time answers, as is the case with self-driving cars and drone navigation.
- Prognosis: NN’s ability to predict based on models has a wide range of applications, including for weather and traffic.
- Fault Tolerance: When significant parts of a network are lost or missing, neural networks can fill in the blanks. This ability is especially useful in space exploration, where the failure of electronic devices is always a possibility.
Tasks Neural Networks Perform
Neural networks are highly valuable because they can carry out tasks to make sense of data while retaining all their other attributes. Here are the critical tasks that neural networks perform:
- Classification: NNs organize patterns or datasets into predefined classes.
- Prediction: They produce the expected output from given input.
- Clustering: They identify a unique feature of the data and classify it without any knowledge of prior data.
- Associating: You can train neural networks to “remember” patterns. When you show an unfamiliar version of a pattern, the network associates it with the most comparable version in its memory and reverts to the latter.
Different Types of Neural Networks
The most commonly used type of Artificial Neural Network is the recurrent neural network. In this system, data can flow in multiple directions. As a result, these networks have greater learning ability. Consequently, they are used to carry out complex tasks such as language recognition.
Other types of Artificial Neural Networks include convolutional neural networks, Hopfield networks, and Boltzmann machine networks. Each network is capable of carrying out a specific task.
Real-World and Industry Applications of Neural Networks
Improving Search Engine Functionality(Google)
During 2015 Google I/O keynote address in San Francisco, Google revealed they were working on improving their search engine.
These improvements are powered by a 30 layer deep Artificial Neural Network.
This depth of layers, Google believes, allows the search engine to process complicated searches such as shapes and colours.
Using an Artificial Neural Network allows the system to constantly learn and improve.
This allows Google to constantly improve its search engine.
Within a few months, Google was already noticing improvements in search results.
The company reported that its error rate had dropped from 23% down to just 8%.
Google’s application shows that neural networks can help to improve search engine functionality.
Similar Artificial Neural Networks can be applied to the search feature on many e-commerce websites.
This means that many companies can improve their website search engine functionality.
This allows customers with only a vague idea of what they want to easily find the perfect item.
Amazon has reported sales increases of 29% following improvements to its recommendation systems.
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