3 Essential Steps to Achieving Optimal Deep Learning Results

Adoption of deep learning has gained a lot of traction during the last two or three years across a wide variety of use cases and has become a top area of interest for many enterprises around the world. Yet, these enterprises still need to determine how best to spend their investment to yield meaningful results for their business. Once an organization has identified the most advantageous use case with which to begin, it is important for CIOs to consider a thoroughly deliberated architectural design. On the one hand, the toolchain for deep learning environments is diverse. There are a wide variety of development toolkits, frameworks and libraries from which to choose. Even the choice of hardware to run the deep learning workload can have a significant impact on an organization's results.