I am trying to find a GRU implementation within DeepLearning4J but cannot seem to find one. Does anyone know if GRU's are implemented within DL4J? If so can you please direct me to an example. If not, is this on their roadmap anywhere?
Thanks
You can't find one because there isn't one. It's not on our roadmap because it doesn't have a clear ROI compared to LSTMs.
We have both LSTMs and Bidirectional.
In general, we've only needed one and people have bene satisfied with that.
Related
I am trying to extract relation between two entities (entity1- relation- entity2) from news articles for stock prediction. I have used NER for entity extraction. It would be great if anyone could help me with relationship extraction.
Relation Extraction is a difficult task in NLP and most of the time there's not a one-size-fits-all solution to that. Depending on the task that you're trying to solve, I would suggest reading some literature about it on Google Scholar and see if there's something similar to what you're trying to do.
Sometimes, authors are kind enough to publish the code of their solution, which are mainly PyTorch/Tensorflow models (hopefully) trained on a specific dataset. One example is this paper.
If you want to stick with Spacy, there are some guides that might help you, but I'm not sure how well it could scale with the task that you need to solve.
Another more basic approach could be to just extract the shortest path between two entities in the semantic graph of a sentence. This might be quite limited, but can be fairly easy to implement.
One final idea that comes to mind is to use encoders and compute the similarity between sentences. If you're doing multi-class classification, this could help solving your problem.
Hope you find something useful among these.
I need to find a deep learning based prediction model, where can I find it?
You can use Pytorch and Tensorflow pretrained models.
https://pytorch.org/docs/stable/torchvision/models.html
They can be automatically downloaded. There are some sample codes, that you can try:
https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#sphx-glr-beginner-blitz-cifar10-tutorial-py
If you are interested in deep learning, I suggest you review the basics of it in cs231n stanford. Your question is a bit odd, because you first need to define your task specifically. Prediction is not a good description. You could look for models for classification, segmentation, object detection, sequence2sequence(like translation), and so on...
Then you need to know how to search through projects on github, and then you need to know python (in most cases), and then use a pretrained model or use your own dataset to train or fine-tune the model for that task. Then you could pray that you have found a good model for your task, after that you need to validate the results on a test set. However, implementation of a model for real-life scenarios is another thing that you need to consider many other things, and you usually need some online-learning strategy, like Federated Learning. I hope that I could help you.
I know the question of GH dynamic edge weights has been raised in various forums, but I still find myself lost on how to implement this. I have seen options like - changing the edge weights and recalculating the contracted graph, disabling contraction hierarchies altogether etc. Could someone please explain this to me from a beginners point of view, like where do I begin, which are the available options, drawbacks of each, the packages and classes in the library that are used to achieve this, etc. Thanks
I would like to look at a couple of implementations of IPMs. The languages preferable are C/C++, Java or any scripting languages like python, perl. Others are also fine.
I am searching for a good resource which can help me with,
basics of optimization techniques,
basics of Interior Point Method and its basics differences with the other techniques,
types of IPMs,
algorithmic details, and
sample implementations.
I am interested in this as part of my project where I would be using these ideas/logic to solve a sys of linear or quadratic equations.
Let me know if you have any info about the above resources.
Another open source interior point linear programming solver is GLPK written in C:
http://www.gnu.org/software/glpk/
and
http://en.wikibooks.org/wiki/GLPK
The linear programming book by Bob Vanderbei (http://www.princeton.edu/~rvdb/LPbook/) is a good book for explaining the use of interior point algorithms for quadratic programming. The cited website also has links to software, but it doesn't seem to be "commercial quality" software. Vanderbei also has LOQO, a more industrial strength interior point code for quadratic programming (http://www.princeton.edu/~rvdb/ps/loqo5.pdf). Another recent idea in interior point qp is: http://www-personal.umich.edu/~murty/Grav-QP.pdf
Simplex methods and interior point methods both have their place. One is not better or faster
than the other in general and you will find that each method performs better on different
classes of problems.
As for codes, the open source Coin-OR project, Clp has Simplex, Dual Simplex, and Interior Point methods implemented in C++.
However, if you are just looking to solve a system of linear or quadratic equations of
the form f(x) = 0, then this is not what you want at all. If the system you want is
linear, then you need to understand direct or iterative linear solvers. If the problem
is nonlinear, you should look into Newton's method or quasi-Newton methods.
best of luck.
First of all, don't compare the simplex method and the interior point method. They have different approaches to solve the problem. The simplex method is used to maximize or minimize the function and the interior point method is used to determine all possible points within the given function which satisfies the set function with delta(very small value) by adding or subtracting it. You can find detailed information regarding them here
[1]: http://www-personal.umich.edu/~murty/Grav-QP.pdf
This is a probably a no brainer, and I've been searching but can't seem to find an answer. What is the term (and any alternate terms) for a graph with only two vertices and only one edge between them?
This is not a homework question :-)
The complete graph on 2 vertices. Denoted K2. See: http://en.wikipedia.org/wiki/Complete_graph
I don't know if an exact term exists, however it is a bipartite complete planar graph with 2 vertices for sure.
From further reading I found Regular Graph entry on wikipedia. It would seem to be a "1-regular graph", though there are other graphs which also qualify as such.