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When should neural networks be used?
Today, neural networks are used for solving many business problems such as sales forecasting, customer research, data validation, and risk management. For example, at Statsbot we apply neural networks for time-series predictions, anomaly detection in data, and natural language understanding.
Why do we need data transformation?
Data is transformed to make it better-organized. Transformed data may be easier for both humans and computers to use. Properly formatted and validated data improves data quality and protects applications from potential landmines such as null values, unexpected duplicates, incorrect indexing, and incompatible formats.
How is data normalization and standardization used in neural networks?
The solution is to encode the non-numerical data and normalize it to be represented as numeric data, this operation is called “Data Encoding and decoding”, the name “Data Standardization” is used too.
When do you need to normalize the distribution of data?
Normalization is useful when your data has varying scales and the algorithm you are using does not make assumptions about the distribution of your data, such as k-nearest neighbors and artificial neural networks. Standardizationassumes that your data has a Gaussian (bell curve) distribution.
When to use normalization instead of standardization in machine learning?
Normalization is preferred over standardization when our data doesn’t follow a normal distribution. It can be useful in those machine learning algorithms that do not assume any distribution of data like the k-nearest neighbor and neural networks.
When to use min max scaling in neural networks?
For Neural Networks, works best in the range 0-1. Min-Max scaling (or Normalization) is the approach to follow. Now on the outliers, in most scenarios we have to clip those, as outliers are not common, you don’t want outliers to affect your model (unless Anomaly detection is the problem that you are solving).