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Deep learning is a very promising, yet complex, area of machine learning. This complexity can both create a barrier to entry for those wanting to get involved in deep learning as well as slow the development of those already comfortable in deep learning.
DeepForge is a development environment for deep learning using Torch focused on alleviating these problems.
As mentioned above, DeepForge focuses on two main goals:
- Improving the efficiency of experienced data scientists/researchers in deep learning
- Lowering the barrier to entry for newcomers to deep learning
It is important to highlight that although one of the goals is focused on lowering the barrier to entry, DeepForge is intended to be more than simply an educational tool; that is, it is important not to compromise on flexibility and effectiveness as a research/industry tool in order to provide an easier experience for beginners (that's what forks are for!).
DeepForge provides a collaborative, distributed development environment for deep learning. The development environment is a hybrid visual and textual programming environment. Higher levels of abstraction, such as creating architectures, use visual environments to capture the overall structure of the task while lower levels of abstraction, such as defining custom layers, utilize text environments to maintain the flexibility provided by torch.
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Intro
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Design Notes