I'm not a data scientist, just a potential end-user who doesn't know what input_shape is.
I believe Google is building or has built some services which you can feed an image or text and it will do some NN magic and spit back the answer.[2]
[1]: https://keras.io/#getting-started-30-seconds-to-keras [2]: https://cloud.google.com/products/machine-learning/
Of course, it’s closed source. But there’s literally just a function Classify, where you hand it pairs of examples and it spits out a model of some automatically chosen kind.
https://github.com/tensorflow/models/tree/master/slim
This is a flexible image recognition framework written in Tensorflow and TFslim
It allows you to train/fine-tune a NN from the command line by specifying only the few details necessary... which NN architecture to use, path to custom data, etc.
1. Kernels (Functions in NNabla) are mostly implemented in Eigen.
2. Network Forward is implemented as sequential run of functions. No multi-threaded scheduling. No multi-GPU or distributed support.
3. Python binding is implemented in Cython.
4. Have some basic dynamic graph support: run functions as soon as you add them to the graph, and run backward afterwards. Somewhat similar to PyTorch.
5. No support for checkpointing and graph serialization, or I'm missing something.
I'm not sure why Sony is releasing this (yet another) deep learning framework. I don't see any new problems the project is trying to solve, compared to other frameworks like TensorFlow and PyTorch. The code is simple and clear, but nowadays people need high-performance, distributed, production-ready frameworks, not another toy-ish framework. Someone please shed some light on me?
BTW, for newcomers to deep learning systems, [CSE 599G1](http://dlsys.cs.washington.edu/) is a good start.
That said, seems like directly using the C++ API was a major use case here, and it looks fairly clean to me.
Maybe, because machine learning is the 2017's big data, cloud, IoT, VR, ...?
https://nnabla.org/ https://nnabla.readthedocs.io
It looks like it's from Sony.
I think every new Deep Learning / NN library should put itself into more context. How does it compare to all the existing frameworks, like TensorFlow, (Py)Torch, CNTK, MXNet, Theano? It actually looks pretty similar, which makes this question even more important. From the examples, it might be most similar to PyTorch with autograd but I'm not sure. So, what are the differences?
[Only half joking.]
Etymology Latin nōmen nesciō ''
