Deep Learning Part 1: Comparison of Symbolic Deep Learning Frameworks

This blog series is based on my upcoming talk on re-usability of Deep Learning Models at the Hadoop Strata World Conference in Singapore. This blog series will be in several parts – where I describe my experiences and go deep into the reasons behind my choices. Deep learning is an emerging field of research, which has its application across multiple domains. I try to show how transfer learning and fine tuning strategy leads to re-usability of the same Convolution Neural Network model in different disjoint domains. Application of this model across various different domains brings value to using this fine-tuned model.