Understanding Memory Mapping in Numpy for Deep Learning: Pt 1

Numpy's memory mapping is a technique that allows users to access small segments of a file without loading the entire file into memory, which is particularly useful when dealing with large arrays in deep learning that may exceed available memory. By saving arrays as Numpy binaries, users can leverage memory mapping to efficiently load and manipulate data, such as training datasets, without overwhelming system resources. The process involves using Numpy's "load()" function with the "mmap_mode" argument, enabling read-only access to large files while occupying minimal memory space. This approach can be beneficial for tasks like model training and prediction, although users must consider operational constraints, such as the need to split data into slices for operations and selecting the appropriate memory mapping mode when modifications are required. Numpy also provides the "memmap()" function, which offers additional features for memory mapping, to be explored further in subsequent discussions.

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