What are Neural Networks?
Neural networks also called artificial neural networks (ANNs) or artificial neural networks (SNNs), are a subset of machine learning and are centers of deep learning algorithms. Their names and structures are influenced by the human brain, just as biological neurons signal each other.
Application of Neural Networks:
Neural networks are widely used, with applications for financial operations, enterprise planning, commerce, business analytics, and product maintenance. These are also widely used in business applications such as forecasting and marketing research solutions, fraud detection, and risk assessment. Neural Network reviews uncertain opportunities to make business decisions based on price data and statistical analysis. The network can distinguish between fine linear interdependence and other methods of technical analysis of patterns.
Types of Neural Networks
Neural Networks can be classified into different types, which are used for different purposes. Although this is not a comprehensive list of types, below will be a representation of the most common types of neural networks that you will come across in terms of their general use:
The Feedforward Neural Networks or Multi-Layer perceptron’s (MLPs)
They consist of an input layer, a hidden layer or layers, and an output layer. Although these neural networks are commonly known as MLPs, it is important to note that they are actually composed of sigmoid neurons, not perceptron’s, as most real-world problems are non-critical.
Conventional neural networks (CNNs)
These are similar to feedforward networks but are more commonly used for image recognition, pattern recognition, and/ or computer vision. These networks use the principles of line linear algebra, specifically matrix multiplication, to identify patterns in an image.
Identifying recurring neural networks (RNNs)
RNN is their feedback loop. These learning algorithms are primarily useful when using time data to make predictions about future results, such as stock market forecasts or sales forecasts.
What are the Limitations of Neural Network?
The topic we need to look at each individual type of network, which is not necessary for this general conversation. However, with regard to backpropagation networks, there are certain issues that consumers should be aware of.
Back productive neural networks are in a sense the ultimate ‘black box. There is no other role to play than to appreciate the general architecture of a network and perhaps extract it with random numbers at first, feeding the user input and watching the train, and waiting for the output. Some software freely available software packages allow the user to sample the ‘development of the network at regular intervals, but the learning progresses on its own.
Backpropagation networks are too slow in training compared to other types of networks and sometimes require thousands of locations. This is not really a problem if running on a parallel computer system, but if the BPN is being developed on a standard serial machine (i.e., the same Spark, Mac, or PC) This may take some time. This is because the CPU of the machine has to count the work of each node and connection separately, which can lead to a large amount of data hassle in very large networks.
Pros and Cons of Neural Network
- These are flexible and regression and classification problems can be used for both.
- They are good at modeling with a large number of inputs with nonlinear data.
- Once trained, the predictions are pretty fast.
- Nerve networks can be trained with any type of input and layers.
- They work best with more data points.
- These are black boxes, meaning we cannot know how much each independent variable is affecting a dependent variable.
- Training traditional CPUs is expensive and time-consuming in terms of computing.
- It relies heavily on training data. It’s more of a hassle to fit and be normal. The mode relies heavily on training data and may be able to get the data.
You may also like to read: Convolutional Neural Network in Deep Learning