Understanding Neural Network
A Neural ( relation to nerve or nervous system) network is a series of Algorithms (set of
instructions, typically solve a class of problems or perform a computation) that endeavors
to recognize underlying relationships in a set of Data through a process that mimics the way
Human Brain operates. Neural networks can adapt to changing input, so the network generates
the best possible result without needing to redesign the output criteria.
Artificial neural network uses the processing of the Brain as a basis to develop Algorithms that
can be used to model complex patterns and prediction problems. A Neural network or an artificial neural network is a sort of computer software, inspired by biological neurons.
In our Brain, there are billions of cells called Neurons which processes information in the form of electric signals. External information/ stimuli is received by the Dendrites ( a short branched extension of a nerve cell, along which impulses received from other cells at synapses are transmitted to the cell body) of the neuron, processed in the neuron cell body, converted to an output and passed through Axon to the next Neuron. The next Neuron can choose to either accept it or reject depending on the strength of the signal.
Artificial Neural Networks (ANN s) have some key advantages that makes them most suitable for certain problems and solutions:
1) Ability to learn and model non-linear and complex relationships, which is really important because in real life many of the relationships between inputs and outputs are non-linear as well as complex.
2) After learning from initial outputs and their relationships, it can infer unseen relationships on unseen Data as well, thus making the model generalize and predict on unseen Data.
3) Unlike other prediction techniques, ANN does not impose any restrictions on input variables (like
how they should be distributed). Many studies have shown that ANNs can better model Data with
high volatility and non-constant variance, given its ability to learn hidden relationships in the Data
without imposing any fixed relationships in the Data. This is very useful in financial forecasting (e.g. Stock Prices) whee Data is very high.
ANNs due to some excellent properties have many applications:
1) Image Processing and Character Recognition.
2) Forecasting.
ANNs have far reaching applications across many fields in Security, Finance, Govt., Defense, Medicine, Banking and Agriculture.which will be discussed in my coming post.
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instructions, typically solve a class of problems or perform a computation) that endeavors
to recognize underlying relationships in a set of Data through a process that mimics the way
Human Brain operates. Neural networks can adapt to changing input, so the network generates
the best possible result without needing to redesign the output criteria.
Artificial neural network uses the processing of the Brain as a basis to develop Algorithms that
can be used to model complex patterns and prediction problems. A Neural network or an artificial neural network is a sort of computer software, inspired by biological neurons.
In our Brain, there are billions of cells called Neurons which processes information in the form of electric signals. External information/ stimuli is received by the Dendrites ( a short branched extension of a nerve cell, along which impulses received from other cells at synapses are transmitted to the cell body) of the neuron, processed in the neuron cell body, converted to an output and passed through Axon to the next Neuron. The next Neuron can choose to either accept it or reject depending on the strength of the signal.
Artificial Neural Networks (ANN s) have some key advantages that makes them most suitable for certain problems and solutions:
1) Ability to learn and model non-linear and complex relationships, which is really important because in real life many of the relationships between inputs and outputs are non-linear as well as complex.
2) After learning from initial outputs and their relationships, it can infer unseen relationships on unseen Data as well, thus making the model generalize and predict on unseen Data.
3) Unlike other prediction techniques, ANN does not impose any restrictions on input variables (like
how they should be distributed). Many studies have shown that ANNs can better model Data with
high volatility and non-constant variance, given its ability to learn hidden relationships in the Data
without imposing any fixed relationships in the Data. This is very useful in financial forecasting (e.g. Stock Prices) whee Data is very high.
ANNs due to some excellent properties have many applications:
1) Image Processing and Character Recognition.
2) Forecasting.
ANNs have far reaching applications across many fields in Security, Finance, Govt., Defense, Medicine, Banking and Agriculture.which will be discussed in my coming post.
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