What is Neural network?
Neural network are a set or bundle of algorithms, designed loosely after the human brain, that are modeled to recognize patterns. They sense or find sensory data through a kind of machine perception, labeling or clustering raw input.
The patterns they recognize are numerical, containing in vectors, into which all real-world data, images, sound, text or time series, must be translated.
Neural networks can adapt to changing input; so that 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.
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.
- Neural networks are the series of algorithms that mimic the operations of a human brain to recognize relationships between vast amounts of data.
- Use of neural networks for stock market price prediction varies.
- They are using in a variety of applications in financial services, from forecasting and marketing research to fraud detection and risk assessment.
Application of Neural Networks
- Neural network are wastly using, with applications for every operations, planning, trading, business analytics and product maintenance.
- Neural network have also gained widespread adoption in business applications such as forecasting and marketing research solutions, fraud or default detection and risk assessment.
- A neural network evaluates price data and unearths opportunities for making trade decisions based on the data analysis.
- Neural network system is very widely use in making decision just like human brain.
How Neural network work?
Neural network are training and taught just like a child is developing brain is trained. They cannot be programmed directly for a particular task and work. They are trained in such a manner so that they can adapt according to the changing input.
There are three methods to teach a neural network:
- Reinforcement Learning
- Supervised Learning
- Unsupervised Learning
Let’s discuss now,
It is type of learning in which learning of input/output mapping is done by regularly interaction with the environment so that the scalar index of performance could be minimized.
Reinforcement learning is a type of Machine Learning where an agent learn how to behave in a environment by performing actions and seeing the results.
Supervised learning means in the presence of a supervisor or teacher. It means a set of a labeled data set is already present with desired output that is the optimum action to be performed by the neural network which is already present for some data sets.
It is a type of system in which both input and desired output data are providing. Input and output data are labelled for classification to provide a learning basis for future data processing
Supervised learning systems provide the learning algorithms with known quantities to support future judgments.
There is no teachers and supervisors are available in this learning, In this Only environment is intract with the machine and from gain knowledge for further work.
This is the neuron that you must be familiar with, if you aren’t you should now be grateful that you can understand this because there are billions of neurons in your brain. There are three components to a neuron, the dendrites, the axon and the main body of the neuron. The dendrites are the receivers of the signal and the axon is the transmitter. Alone, a neuron is not of much use, but when it connect to other neurons, it does several complicated computations and helps operate the most complicated machine on our planet, which is human body.
How neural network work
Our brain essentially have five basic input parameters, which are our senses to touch, hear, see, smell and taste. The neurons in the brain create more complicated parameters such as emotions and feelings, from these basic input parameters.
And our emotions and feelings, make us act and take decisions which is basically the output of the neural network of our brains. Therefore, there are two layers of computations in this case before making a decision.
The first layer takes in the five senses as inputs and results in emotions and feelings, which are the inputs to the next layer of computations, where the output is a decision or an action.
Hence, in this extremely simplistic model of the working of the human brain, we have one input layer, two hidden layers, and one output layer.
There are three input parameters as shown in the diagram, the hidden layer consists of 5 neurons and the resultant in the output layer is the prediction for the stock price. The 5 neurons in the hidden layer will have different weights for each of five input parameters and might have different activation functions, which will activate the input parameters according to various combinations of the inputs.
The first neuron might be looking at the volume and difference between the Close and the Open price and might be ignoring the High and Low prices. In this case, the weights for High and Low prices will be zero.
Based on the weights that the model has train itself to attain, an activation function will be applying to the weighted sum in the neuron, this will result in an output value for that particular neuron or neural network.
Similarly, the other two neurons will result in an output value based on their individual activation function and weights. Finally, the output value or the predicted value of the stock price will be the sum of the three output values of each neuron. This is how the neural network will work to prediction stock prices.
Advantages of Neural Network
- Have ability of fault tolerance.
- Have a distributed memory
- Can work with incomplete information once trained.
- Can make machine learning.
- Parallel processing.
- Stores information on an entire network
- Can learn non-linear and complex relationships.
- Probability and statistics.
- Distributed computing.
- Fundamental programming skills.
- Knowledge of applied maths and algorithms.
- Data modeling or evaluation.
- Software engineering and system design.
Neural network scope and career
It has a wide scope in the future. Researchers are constantly working on new technologies based on neural networks. Everything is converting into automation hence they are very much efficient in dealing with changes and can adapt accordingly.
There is huge career growth in the field of neural networks. An average salary of neural network engineer ranges from $33,856 to $153,240 per year approximately.
Due to increase in new technologies, there are many job openings for engineers and neural network experts.
Hence in future also neural networks will prove to be a major job provider.
I think that is very important information for you guys because neural network is going to increase in the future.
And career in this, Outstanding in the future because coming world fill with the robotics and everything work with the automation so study of the neural network is very important….!
You can learn neural network from here