Advances in Deep Learning for Image Analysis
Cloudera Fast Forward Labs’ latest applied machine learning research report focuses on advancements in Deep Learning for Image Analysis. Research and commercial interest in deep learning has exploded in the last five years, driving remarkable advancements across applications including medical imaging, autonomous vehicles, news and media (including manipulation), and art.
While increasingly capable algorithms and tools including open source toolkits and commercially available solutions are accelerating applications, there are considerable risks associated with shortcomings in their effective implementation. In this update to our 2016 report, we revisit the state of the art on Deep Learning for Image Analysis to unlock the evolving landscape of image analysis (IA) algorithms, open source IA toolkits, and the emergence of concrete standards of practice around IA development.
This report enables data scientists and business stakeholders with practical guidance for creating effective, safe and ethical image analysis product capabilities. With our accompanying prototype, ConvNet Playground, we demonstrate how deep learning models can be applied to the task of semantic image search and provide tools that build intuition on how the models work.