OpenDeep is a general purpose deep learning package that bridges the gap between industry and state-of-the-art research.
We are a research library developed for real-world applications and industry use.
Modularity. A lot of recent deep learning progress has come from combining multiple models. Existing libraries are either too confusing or not easily extensible enough to perform novel research and also quickly set up existing algorithms at scale. This need for transparency and modularity is the main motivating factor for creating the OpenDeep library, where we hope novel research and industry use can both be easily implemented.
Ease of use. Many libraries require a lot of familiarity with deep learning or their specific package structures. OpenDeep's goal is to be the best-documented deep learning library and have smart enough default code that someone without a background can start training models. This motivation will lead to a series of easy to understand tutorials for the different modules in the library.
State of the art. A side effect of modularity and ease of use, OpenDeep aims to maintain state-of-the-art performance as new algorithms and papers get published. As a research library, citing and accrediting those authors and code used is very important to the library.
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