January 2019
Intermediate to advanced
322 pages
7h 29m
English
Data can come from many sources and in many types, for example:
When working with neural nets, the end goal is to convert each data type into a collection of numerical values in a multidimensional array. Data could also need to be pre-processed before it can be used to train or test a net. Therefore, an ETL process is needed in most cases, which is a sometimes underestimated challenge that data scientists have to face when doing ML or DL. That's when the DL4J DataVec library comes to the rescue. After data is transformed through this library API, it comes into a format (vectors) understandable by neural networks, so DataVec ...