The document discusses strategies for parallelizing large-scale deep learning neural networks on distributed systems like Apache Spark. It describes four main types of parallelization: inter-model parallelism by exploring different hyperparameter models in parallel; data parallelism by distributing data across identical models and averaging parameters; intra-model parallelism by partitioning layers of a single large model; and pipelined parallelism by processing samples in an assembly line fashion through layers. The strategies aim to speed up model training by leveraging multiple computing resources.