Introduction to Neural Networks:
First of all, let us know what is a neural network? So neural network is a process in the field of Artificial Intelligence that enables computers to process data in a similar fashion just like the human brain does the processing of data. Moreover, the name and structure of neural networks are inspired by the human brain.
Neural networks are known by other names like artificial neural networks or ANNs in short as well as simulated neural networks or SNNs. Also, artificial neural networks are built by combining node layers. Talking about node layers in ANNs, there is one input layer, one or more hidden layers and one output layer.
Here you can consider each node in neural networks as artificial neurons. Moreover, neural networks depends on training data to learn and upgrade their accuracy over a period of time. Also, the most popular example of neural networks is the search algorithm of Google.
Working of Neural Networks:
As mentioned earlier, human brain is the inspiration behind neural networks. Coming to human brain, there is a complex network of neurons are present. Neurons are the smallest working unit of the brain. And they communicate and transfer information among themselves with the help of chemical and electrical signals. Overall these altogether help human beings to process information, take decisions, think about something so on and so forth.
Likewise, in neural networks there is the existence of artificial neurons. And these artificial neurons work together to solve problems in neural networks. Moreover, artificial neurons are software modules and are known as nodes. The neural networks are nothing but software programs or algorithms that uses computational systems to solve mathematical problems.
Important terminologies related to Neural Networks
Weights: In a simple and straightforward language, weights in neural networks are numerical values that gets multiplied by inputs.
Activation Function: Another important term in artificial neural networks is activation function. It is a mathematical formula that enables artificial neurons to switch ON/OFF.
Bias: Bias value allows to shift the activation function to the left or to the right. It can be a critical factor in the matter of successful learning of neural networks.
Input Layer: In this layer of neural networks the initial data is provided.
Hidden Layers: Hidden layers are present in between input layer and the output layer in artificial neural networks. Also, one should note a point about Hidden layers is that, it is the place where all computational activities are performed.
Output Layer: This layer in ANN generates the output for the provided inputs.
Types of Neural Networks:
- Perceptron: It is the oldest and most basic type of neural networks. It contains only one artificial neuron. In which it takes the input and then it applies an activation function on the input and generates output in binary form.
- Feed Forward Neural Network: Feed Forward Neural Network contains many artificial neurons as well as multiple hidden layers connected with each other. The reason why this neural network is called “Feed Forward” is because in this, flow of data takes place in forward direction only. There is no chance of backward propagation. It is used in classification, face recognition, pattern recognition and speech recognition.
- Multi-layer perceptron: This type of ANN contains multiple hidden layers as well as activation functions. In this learning occurs in a Supervised way in which the weights are updated as per Gradient Descent. In which Gradient Descent is an algorithm for optimization and used mainly to train neural networks and machine learning models.
- Radial Basis Networks: When talking about Radial Basis Networks, it contains an input layer, a layer of RBF neurons and an output layer. In RBN ,radial function is used as an activation function.
- Convolutional Neural Networks: Convolution Neural Networks are used much in the task of image classification. The important features of the image are taken out by the help of multiple convolutional layers present in CNN. Moreover, Convolutional Neural Network uses Rectified Linear Unit( in short ReLU).
- Recurrent Neural Networks: This neural network is suitable when there is a need to do predictions by using sequential data. The sequential data can be anything like the sequence of words, images so on and so forth.
Pros and cons of Artificial Neural Networks:
Pros:
- Capable of processing unorganized data: One of the biggest advantages of Artificial Neural Networks is that they can process unorganized data. The ANN does the task of processing, segregating as well as categorization of unorganized sets of data. By this way neural networks convert unorganized data into organized form of data.
- Adaptive structure of Artificial Neural Network: Second benefit of neural networks is that its structure is adaptive in nature. Let me elaborate this, by word adaptive regarding ANN, I mean to say that it adapts, transforms and adjusts itself according to the environment or as per purpose and gives results accordingly.
- Implementation benefits: Third benefit of neural network is that it can be implemented or executed in any type of application.
- Functional even if data contains errors: The fourth benefit of ANN is that it remains in functional state even if there are some kind of errors or noise in the data.
- Multitasking: When neural networks are trained to work on multiple tasks at the same time,then after the training period these neural networks become multitaskers. As a result they prove to be useful in AI applications.
Cons:
- Enormous Training Data: A trained neural network can perform tasks very easily without much involvement of programmers. But one of the demerits of neural networks is that to learn, they require a huge amount of training datasets. Collecting large amounts of training datasets for neural networks can be a time taking and difficult process.
- Computing requirements: The second demerit of artificial neural networks is that they require capable hardware parts and in large quantities. For example they require dedicated AI accelerators or central processors, huge storage space and so on and so forth.
- High Processing Time: The third demerit is that, If the neural networks are big enough then it will require high processing time.
- Costly Setup: As I have mentioned earlier for the neural networks, high-end computing hardware is required and this ultimately leads to a costly setup. Its high cost can also be the deciding factor for whether to go for neural networks or not.
- Other Limitations: Neural networks are not much efficient in the problems that require decision-making or reasoning. Moreover, neural networks are not capable of answering why they have taken a particular decision.
So this was about neural networks. What are your thoughts mention it in comments below. Thanks for reading and I will come up with another article on this blog. And keep visiting Novadroid360.
