Hello everyone, I am Weixue AI. Today I will introduce you to the application of deep learning skills 8-loading and processing of various data types, and input neural networks for training. In model training, it is often difficult for everyone to start with various data types. For unstructured data that is already complex, special processing is required. Here is how we process data to input it into the model for training. To put it bluntly, it is to convert the data to 0, 1, so that the computer can understand the data. I have summarized three types of data, and I will continue to summarize them later.
1. The steps of data processing in model training:
1. Data reading: read data from data sources (such as files, databases, etc.).
2. Data preprocessing: process the original data to make it more suitable for model training. This may include operations such as feature extraction, normalization, and scaling.
3. Data division: Divide the preprocessed data into training set, validation set and test set for training and evaluation.
4. Data enhancement: For data types such as images and videos, the data set can be expanded by methods such as rotation, cropping, and translation to increase the generalization ability of the model.
2. Sample structured data
Suppose we have a dataset containing information about houses such as square footage, number of bedrooms, number of floors, etc. We can use the pandas library to read data files in CSV format and preprocess the data. Data samples: