Summary

In contrast to the traditional machine learning algorithms, deep learning models have the capability to address the challenges imposed by a massive amount of input data. Deep learning networks are designed to automatically extract complex representation of data from the unstructured data. This property makes deep learning a precious tool to learn the hidden information from the big data. However, due to the velocity at which the volume and varieties of data are increasing day by day, deep learning networks need to be stored and processed in a distributed manner. Hadoop, being the most widely used big data framework for such requirements, is extremely convenient in this situation. We explained the primary components of Hadoop that are essential for distributed deep learning architecture. The crucial characteristics of distributed deep learning networks were also explained in depth. Deeplearning4j, an open source distributed deep learning framework, integrates with Hadoop to achieve...