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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.
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I am working on classification problem for Covid19, Pneumonia and healthy lung.
I have 3000 images for each class.
Can I apply vision transformers for this image classification instead of normal CNN?
Or is there any prerequisite for applying this? I am new to transformers.
I have tried all CNN and they have achieved 95% accuracy till now.
At small to medium datasets, ViTs don't give a performance that is comparable. On really big datasets, however, they have outperformed CNNs. More information here https://www.v7labs.com/blog/vision-transformer-guide
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I've been a full stack web developer for 15 years now and would like to be involved in machine learning. There is already a specific scenario for this: We have a database with several million products and one product image each. There is also a database with about 5000 terms.
A product image is linked to several terms (usually 3 - 20), whereby the link still has a weighting (1-100%). The terms are always of a visual nature, that is, they describe a visually recognizable feature on the image.
The aim should now be to upload a new image (of course with thematic reference) and to get an answer with possible terms (including probability) based on the already classified images.
Do you have any advice on how best to start here? Is there a framework that comes close to this scenario? Is TensorFlow relevant for this task? What new language should I learn?
Thank you very much!
TensorFlow can be used, it's pretty "low-level" though. So if you're just starting out you might be better off using Keras with a TensorFlow backend as it's more userfriendly.
Regarding languages you will probably use Python. So if you don't know it already you should get started. In my opinion you can also learn it on-the-fly while practicing as you're already a developer.
As for tutorials you will have to probably pick out the relevant bits of many different tutorials. You could get started with something like this:
https://www.pyimagesearch.com/2018/05/07/multi-label-classification-with-keras/
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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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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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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.