What is Neural Networks?
The computing systems inspired from biological neural networks to perform different tasks with huge amount of data involved is called artificial neural networks or ANN. Different algorithms are used to understand the relationships in a given set of data so that best results could be produced from the changing inputs. The network is trained to produce the desired outputs and different models are used to predict the future results with the data. The nodes are interconnected so that it works like a human brain. Different correlations and hidden patterns in raw data is used to cluster and classify the data.
Understanding Neural Network
Neural networks are trained and taught just like a child’s developing brain is trained. They cannot be programmed directly for a particular task. They are trained in such a manner so that they can adapt according to the changing input. There are three methods or learning paradigms to teach a neural network.
- Supervised Learning
- Reinforcement Learning
- Unsupervised Learning
Let us discuss them in brief,
1. Supervised Learning
As the name suggests, supervised learning means in the presence of a supervisor or a teacher. It means a set of a labeled data set is already present with desired output i.e. the optimum action to be performed by the neural network which is already present for some data sets. The machine is then given new data sets to analyze the training data sets and to produce the correct output.
It is a closed feedback system, but the environment is not in the loop.
2. Reinforcement Learning
In this, learning of input-output mapping is done by continuous interaction with the environment so that the scalar index of performance could be minimized. In this, instead of a teacher, there is a critic that converts the primary reinforcement signal i.e. the scalar input received from the environment into heuristic reinforcement signal (higher quality reinforcement signal) also a scalar input.
The goal of this learning is to minimize the cost to go function i.e. the expected cumulative cost of actions taken over a sequence of steps.
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3. Unsupervised Learning
As the name suggests, there is no teacher or supervisor available. In this, the data is neither labeled nor classified and no prior guidance is available to the neural network. In this, the machine has to group the provided data sets according to the similarities, differences, and patterns without any training provided beforehand.
Working with Neural Network
The neural network is a weighted graph where nodes are the neurons and the connections are represented by edges with weights. It takes input from the outside world and is denoted by x(n).
Each input is multiplied by its respective weights and then they are added. A bias is added if the weighted sum equates to zero, where bias has input as 1 with weight b. Then this weighted sum is passed to the activation function. The activation function limits the amplitude of the output of the neuron. There are various activation functions like Threshold function, Piecewise linear function or Sigmoid function.
The Architecture of Neural network
There are basically three types of architecture of the neural network.
- Single Layer feedforward network
- Multi-Layer feedforward network
- Recurrent network
1. Single- Layer Feedforward Network
In this, we have an input layer of source nodes projected on an output layer of neurons. This network is a feedforward or acyclic network. It is termed as a single layer because it only refers to the computation neurons of the output layer. No computation is performed on the input layer hence it is not counted.
2. Multi-Layer Feedforward Network
In this, there are one or more hidden layers except for the input and output layers. The nodes of this layer are called hidden neurons or hidden units. The role of the hidden layer is to intervene between the output and the external input. The nodes of the input layer supply input signal to the nodes of the second layer i.e. the hidden layer, and the output of the hidden layer acts as an input for the next layer and this continues for the rest of the network.
3. Recurrent Networks
A recurrent is almost similar to a feedforward network. The major difference is that it at least has one feedback loop. There might be zero or more hidden layer but at least one feedback loop will be there.
Advantages of Neural Network
- Can work with incomplete information once trained.
- Have ability of fault tolerance.
- Have a distributed memory
- Can make machine learning.
- Parallel processing.
- Stores information on an entire network
- Can learn non-linear and complex relationships.
- Ability to generalize i.e. can infer unseen relationships after learning from some prior relationships.
Required Neural Network Skills
- Knowledge of applied maths and algorithms.
- Probability and statistics.
- Distributed computing.
- Fundamental programming skills.
- Data modeling and evaluation.
- Software engineering and system design.
Why should we use Neural Networks?
- It helps to model the nonlinear and complex relationships of the real world.
- They are used in pattern recognition because they can generalize.
- They have many applications like text summarization, signature identification, handwriting recognition and many more.
- It can model data with high volatility.
Neural Networks Scope
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. Due to an 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.
How this technology will help you in career growth
There is a 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.
Conclusion
There is a lot to gain from neural networks. They can learn and adapt according to the changing environment. They contribute to other areas as well as in the field of neurology and psychology. Hence there is a huge scope of neural networks in today’s time as well as in the future.
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