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I actually strongly agree with him, In fact the progress in Deep Learning has stalled due to inefficiencies in sharing data and code between researchers. Today
by aub3bhat 9y ago
I actually strongly agree with him, In fact the progress in Deep Learning has stalled due to inefficiencies in sharing data and code between researchers. Today to replicate a study you have to spend several days downloading data, making sure all dependencies are installed etc. This is worsened by lack of standard formats for even simple things such as representing 2D bounding box on an image.
People forget that the reason Deep Learning came to prominence was engineering that enabled use of GPU's (Thanks to Alex K.) and large dataset from ImageNet competition (Thanks to Fei-Fei Li).
Importance of software engineering in moving the field forward is often under-appreciated. This blogpost beautifully illustrates several instances where a great implementation made it easier for researcher to speed up experimentation and lead to breakthroughs in Computer Vision. [1]
I am building Deep Video Analytics which aims to become the Rail/Django/MySQL/(favorite analogy) of Visual data Analytics [2,3].
[1] http://www.computervisionblog.com/2015/01/from-feature-descriptors-to-deep.html http://www.computervisionblog.com/2015/01/from-feature-descr...
[2] https://github.com/AKSHAYUBHAT/DeepVideoAnalytics/ https://github.com/AKSHAYUBHAT/DeepVideoAnalytics/
[3] http://www.deepvideoanalytics.com/ http://www.deepvideoanalytics.com/
- orthoganol 9y agoWell, my professional experience has been completely different, maybe hardcore research in images is different. At any rate isn't TF already the Rails/ magical framework of DL, in some sense?
- ericd 9y agoI'm not an expert, but Keras seems much closer than TF.
- espadrine 9y agoThey're complementary. Keras typically uses TF as a back-end, letting users model standard architectures quickly, while TF lets you express new and complex graphs of computation.
- ericd 9y agoI'm aware, I'm using Theano as my backend. I just mean that Keras seems more analogous to Rails than TF - TF would be Ruby in that analogy.
- shadowmint 9y agoI don't know... There's plenty of horrible code out there, particularly tensorflow code, which matches all the classical metrics for 'spaghetti code'. The complexity of the implementation might be hidden away by the framework, but there's no excuse for writing massive hundred+ line functions that do multiple things with copy-pasted code blocks. That's just bad code, in ML or not.
- killjoywashere 9y agoI also strongly agree with him, and you. The work of annotating, curating, prepping, etc, is huge. Every industry could split off 10% of its workforce just to work on prepping their data for ingestion, and then designing the interfaces to actually use the inferences the algorithms emit.