Let's discuss the most important part of any machine learning puzzle: data preprocessing and normalization. Garbage in, garbage out would be the most appropriate statement for this situation. The more noise we let pass through, the more undesirable outputs we will receive. Therefore, you need to remove noise and keep signals at the same time.
Another challenge is handling various types of data. We need to convert raw datasets into a suitable format that a neural network can understand and perform scientific computations on. We need to convert data into a numeric vector so that it is understandable to the network and so that computations can be applied with ease. Remember that neural networks are constrained to only one type of data: vectors.
There has to be an approach regarding how data is loaded into a neural network. We cannot...
