data preprocessingfeature engineeringand feature learning in addition to model evaluationan important question we must tackle before we dive deeper into model development is the followinghow do you prepare the input data and targets before feeding them into neural networkmany data-preprocessing and feature-engineering techniques are domain specific (for examplespecific to text data or image data)we'll cover those in the following as we encounter them in practical examples for nowwe'll review the basics that are common to all data domains data preprocessing for neural networks data preprocessing aims at making the raw data at hand more amenable to neural networks this includes vectorizationnormalizationhandling missing valuesand feature extraction vectorization all inputs and targets in neural network must be tensors of floating-point data (orin specific casestensors of integerswhatever data you need to process--soundimagestext--you must first turn into tensorsa step called data vectorization for instancein the two previous text-classification exampleswe started from text represented as lists of integers (standing for sequences of words)and we used one-hot encoding to turn them into tensor of float data in the examples of classifying digits and predicting house pricesthe data already came in vectorized formso you were able to skip this step value normalization in the digit-classification exampleyou started from image data encoded as integers in the - rangeencoding grayscale values before you fed this data into your networkyou had to cast it to float and divide by so you' end up with floatingpoint values in the - range similarlywhen predicting house pricesyou started from features that took variety of ranges--some features had small floating-point valuesothers had fairly large integer values before you fed this data into your networkyou had to normalize each feature independently so that it had standard deviation of and mean of in generalit isn' safe to feed into neural network data that takes relatively large values (for examplemultidigit integerswhich are much larger than the initial values taken by the weights of networkor data that is heterogeneous (for exampledata where one feature is in the range - and another is in the range - doing so can trigger large gradient updates that will prevent the network from converging to make learning easier for your networkyour data should have the following characteristicstake small values--typicallymost values should be in the - range be homogenous--that isall features should take values in roughly the same range licensed to