Artificial Neural Networks:

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Artificial neural networks (ANN) generally known as Neural networks that enumerate the computing systems virtually stimulated by biological neural networks that initiate animal and human brains.


⦁ Neural networks are quite a resemblance to Human brains.

⦁ Artificial Neural Networks are a group of collective units and nodes known as artificial neurons.


⦁ Neural network knowledge is stored within the interconnection strengths known as synaptic weight.

⦁ Activation functions are implemented in (ANN).

⦁ These networks are determined by utilizing their architecture and algorithms.

Neural networks are embedded with the computational learning system with the help of neural functions and convert the complex data sets into the desired output with ease according to the requirements.

Neural networks play a significant role in Deep learning. Both Neural networks and deep learning are both inter-linked to one another. Currently, these both are in a stage where many unsolved theories and applications are resolved with efficient solutions and accurate.

Importance of Neural Networks:

1. Neural networks are also supremely suited to help people solve complex problems in a real-life scenario.

2. These are helpful and handy in fraud detection techniques.

3. It deals with highly volatile data.

4. Risk assessment can be done to save troubles in later stages.

⦁ There are several kinds of Neural Networks at present and few of them are in the state of development.

Neural Networks are classified according to their characteristics:

⦁ Structure

⦁ Data flow

⦁ Density

⦁ Layers

⦁ Depth activation filters

⦁ Neurons, and many more.

Perceptron:


This model was proposed by Minsky-Papert and it was considered as the simplest and oldest model of Neuron.

It is a supervised learning algorithm where the classification of data is done in two types hence it is named as a binary classifier.

It is also known as the Threshold Logic Unit(TLU).

Perceptron has a unique capability to separate the input spaces in two categories by a Hyperplane concerning the following equation: WTX + bi = 0

Logical gates implementation is acquired in Perceptron.

Feedforward Neural Networks:

It is the simplest form of Artificial Neural network where the data transmission is done in a single direction.


It consists of majorly 3 layers :

⦁ Input layer

⦁ output layer.

⦁ Hidden layer.

In this method, the input data is passed into a layer where the calculations are performed.

During the state of processing elements computation occurs and the concept of weight sums of inputs is applied.

This process is continued until the desired output is obtained.

Hence it is a Uni-directional and only undergoes forward propagation.

These Networks are highly responsive to Noisy data.

Weights are adjusted properly utilizing Non-linear optimization known as gradient descent.

Multilayer Perceptron:


⦁ It is considered as an opening to the complex neural nets where the data transmission flows through artificial neurons.

⦁ This mechanism undergoes bi-directional propagation.

⦁ Forward and backward propagation.

⦁ In this perceptron, every single node is fully connected to the next Neural network.

⦁ These networks are self-distinguished by themselves based on predicted output and training inputs.

⦁ Non-linear activation functions are in this layer.

Applications:

⦁ speech recognition

⦁ machine translation

⦁ data mining

⦁ complex classification

⦁ image recognition.


Real-Time Applications Implementation With the Help of Neural Networks:

⦁ Image processing

⦁ Language processing and detection

⦁ Speech recognition

⦁ Forecasting

Advantages of Artificial Neural Networks:

⦁ Neural networks have been successfully applied to a broad spectrum of data-intensive and real-life applications in our day to day life.

⦁ Neural networks have a remarkable ability to learn themselves and induce the output.

⦁ The data is stored in networks instead of relying on databases.

⦁ The lame light to the Artificial Neural Networks is obtained in recent years by AI.

⦁ Neural networks are preferred as more effective in solving complex problems.

⦁ Multiple tasks are performed with ease without affecting the system performance.

⦁ These networks analyze the work and replicate their tasks similar to Human brain functioning.

⦁ Fault tolerance in the cells of ANN is neglected and doesn’t affect the output generation.

⦁ It has a unique ability to train a machine from specific events and generate decisions.

⦁ It is capable of grasping hidden relationships in data without commanding any standard relationships and replicate highly Volatile data and non-constant variance.

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