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Our boss found the idea of the paper "A Neural Algorithm of Artistic Style" amazing and think it should attract some of his customers. He decided to set up a server provide the service of style transfer for them.
There are several deep learning architectures with implementations of this idea such as TensorFlow, Torch, caffe, etc. If aims to achieve the best performance, which implementation of these architectures runs fastest? If we configure the algorithm with a considerably good CUDA device such as GeForce GTX 1090 or better, is it possible to finish the task of a VGG model in several seconds? If wish to apply the state of art of the idea to the aforementioned deep learning architectures, are they all applicable?
Checking out some benchmarks: https://github.com/soumith/convnet-benchmarks I'd say that Nervana and Torch are the best frameworks.
If instead of time, we have a look at open source contributions and paper implementations I think torch is the winner.
You can easily find neural-style algorithm implementations in Torch: Neural-Style and Fast Neural-Style
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Recently I decided to learn MXNet, as some code I need to use, is written using this API.
However, I would like to know which are the advantages and disadvantages of MXNet compared to the other Deep Learning Libraries out there.
Perhaps the biggest reason for considering MXNet is its high-performance imperative API. This is one of the most important advantages of MXNet to other platforms. Imperative API with autograd makes it much easier and more intuitive to compose and debug a network. PyTorch also supports imperative API, but MXNet is the only platform AFAIK that supports hybridization, which effectively allows your imperative model to be converted to a symbol for similar performance to symbolic API. Here is a link to tutorials on Gluon, MXNet's imperative API: http://gluon.mxnet.io/
Given that you're using an example code, it is possible that the example was written using symbolic API. You may notice MXNet's advantage in symbolic API when training on many GPUs. Otherwise you won't notice much of a difference (except perhaps in some memory usage).
Tensorflow does have a one year head-start to MXNet and as a result it has a larger user base, but it only supports symbolic API (imperative API is very new and is only meant for experimentation), which is significantly harder to debug a network when you run into issues. However MXNet has quickly caught up in features and with 1.0 release, I don't think there is anything in TF that MXNet doesn't support.
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Need to start an object detection project. can anyone suggest the better framework which has better accuracy and speed. I have read about imagenet, resnet, mobilenet, yolo, tensorflow and dlib features. Can anyone give a comparison of them and suggest a better option.
A good overview is described in "Speed/accuracy trade-offs for modern convolutional object detectors" (https://arxiv.org/abs/1611.10012).
In order to save time, you may consider using Google Object Detection API https://github.com/tensorflow/models/tree/master/research/object_detection, they have an tutorial on how to train on your own dataset.
It is hard to say which object detection framework is the best. However, I saw people usually stick to Faster R-CNN (for accuracies) and SSD or YOLOv2 (for speed).
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Most of my training is done using RNN , either LSTM or GRU, and I've found the CPU to be taking much of the load.
I'm looking to put a new system together for testing, and I haven't seen any posts on which Architecture is more conducive to ML with TensorFlow. It seems it boils down to the Intel Core-X series having AVX-512 and AMD not (Specifically looking at i9-7900X vs Threadripper 1950X as they are a similar price).
So the question I have is two-fold:
Does TensorFlow make use of AVX-512 extensions, and
Are those extensions beneficial enough to make up for a 6-core deficiency of the 1950X -> 7900X
~ Are there any other considerations I am not taking into account? Any specialized performance optimizations that TF has made for Intel processors over AMD?
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Deep-learning is applied to play game in some projects, so it should make sense to apply it for UI test automation. Any comments on this idea?
In large software, it is really hard and time taking to test out all possible scenarios via traditional approach. So, building a machine learning model to do this is a neat idea. The end goal for such a system would be to crash to make the software unresponsive.
There is research being done on this idea. You should take a look at this research paper which explores Reinforcement Learning as an approach to automated GUI robustness testing. Reinforcement Learning is also one of the approaches used in teaching computers to play games.
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I have read blogpost about TensorFlow is being open sourced.
In the tutorials and the examples on the TensorFlow website, I see that they are mostly classification problems. (e.g. given an image, classify the number written in it)
I am curious about it the software also suitable for solving problems in recommendation system?
For example, is it good for solving problems on collaborative filtering / content-based filtering?
Tensorflow is great for deep learning, or training large neural nets. Although, it can be used for several other mathematical applications such as PDEs, various classifiers, recommendation systems etc, there doesn't seem to have a lot of support for them as yet.
This reddit thread might be a good place to start for searching libraries which are centred around recommendation systems.