To train the deep learning model efficiently using Apache Spark, you would first partition the large dataset into smaller chunks and distribute them across the Spark cluster. Then, you would use Spark's data parallelism to train the model on each partition in parallel. Finally, you would aggregate the results from each partition to update the model parameters.

To train the deep learning model efficiently using Apache Spark, you would first load the entire large dataset into memory. Then, you would use Spark's data parallelism to train the model on the dataset in parallel. Finally, you would update the model parameters based on the results from each partition.

To train the deep learning model efficiently using Apache Spark, you would first partition the large dataset into smaller chunks and distribute them across the Spark cluster. Then, you would use Spark's model parallelism to train the model on each partition sequentially. Finally, you would aggregate the results from each partition to update the model parameters.

To train the deep learning model efficiently using Apache Spark, you would first partition the large dataset into smaller chunks and distribute them across the Spark cluster. Then, you would use Spark's data parallelism to train the model on each partition sequentially. Finally, you would update the model parameters based on the results from each partition.