## What is Neural Networks?

Neural Networks are modeled after the human brain to recognize patterns. They take data sets and recognize the pattern. They help to group unlabeled data on the basis of the similarities i.e. they help in classification and clustering. They can adapt to changes and generates the best possible result without the need to redesign the criteria of output.

### Definition Of Neural Network

The neural network is a set of algorithms patterned after the functioning of the human brain and the human nervous system. A neuron is a mathematical function that takes inputs and then classifies them according to the applied algorithm. It is consists of an input layer, multiple hidden layers, and an output layer. It has layers of interconnected nodes. Each node is a perception that feeds the signal into an activation function.

### 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.

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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.

#### 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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