Neural Networks: Introduction - How is it different from Logistic Regression
Neural Networks are a class of algorithms that is used widely for many purposes. It mimics the functioning of the brain (Neurons, hence the name) and try to simulate the network in the human brain to teach / train a computer.
Why not logistic regression: Logistic regression is also a class of algorithm pretty much used for solving similar set of problems as is the case with neural networks, but there are variour constraints with Logistic Regression. Logistic Regression is typically for a small set of features, where all the polynomial terms can be included in the model ($x_1^2x_2, x_1x_2^2$, etc). The problem occurs when we have too many features : what if we have 100 variables, and wee need every combination of all the variables which will include terms like $x_{23}^3x_{74}^7x_{12}^{33}$ and so on. It becomes extremely hard to create and analyze these features. Also, this will lead to the problem of overfitting, and these computations will be extremely computationally expensive. To solve this, if we reduce the number of features, we will lose information about the data.
Neural Networks aim to solve this issue. It is built to optimize for evaluating a huge number of features which is a typical case in any of the image recognition or handwriting recognition problem. It can be used for simple classification, multiclass classification or prediction models.
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Neural Networks are a class of algorithms that is used widely for many purposes. It mimics the functioning of the brain (Neurons, hence the name) and try to simulate the network in the human brain to teach / train a computer.
Why not logistic regression: Logistic regression is also a class of algorithm pretty much used for solving similar set of problems as is the case with neural networks, but there are variour constraints with Logistic Regression. Logistic Regression is typically for a small set of features, where all the polynomial terms can be included in the model ($x_1^2x_2, x_1x_2^2$, etc). The problem occurs when we have too many features : what if we have 100 variables, and wee need every combination of all the variables which will include terms like $x_{23}^3x_{74}^7x_{12}^{33}$ and so on. It becomes extremely hard to create and analyze these features. Also, this will lead to the problem of overfitting, and these computations will be extremely computationally expensive. To solve this, if we reduce the number of features, we will lose information about the data.
Neural Networks aim to solve this issue. It is built to optimize for evaluating a huge number of features which is a typical case in any of the image recognition or handwriting recognition problem. It can be used for simple classification, multiclass classification or prediction models.
-1.svg.png)
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