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Hey I want to use word2vec algorithm without implementing it (I saw a lot of places that teaches how to implement one).
does anyone can tell me what is the best lib to use?
I saw there is Genesim, and Deeplearning4j. also TensorFlow but I can't find a place where they have the function that I need (only how to implement with this lib).
can someone give some comparison about efficiency? how easy to use? the word2vec algorithm for each lib?
any helpful tip or resources would be great.
SpaCy comes with pretrained word embeddings that you can use - it's very easy to use, you can find examples on how to download the embedding and use it here.
The implementation in the Python gensim library offers a Word2Vec model class that is flexible (with options not available elsewhere) & as fast as the original word2vec.c code released by the Google researchers who created the algorithm.
You can see its docs at:
https://radimrehurek.com/gensim/models/word2vec.html
There's an intro tutorial that can be run as in a 'Jupyter' notebook:
https://radimrehurek.com/gensim/auto_examples/tutorials/run_word2vec.html
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I am working on a project to use a pre-trained model and finetune it for customized language translations, for example from English to French. Is it possible to load these models in Tensorflow and run them to see how translations turn out and fine-tune afterward?
Probably the fastest way to do so is relying on the HuggingFace transformers library. If you're not familiar with it, you may take a look at their official documentation. To fine-tune a BART for NMT you can use directly this provided script (it works with some other pre-trained models too).
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I'm new to YOLO and trying to make car counting application using YOLO. The cars is from video file. Is there any reference? Thank you
There are multiple places from which I suggest you try to learn:
1) The CNN course from coursera https://www.coursera.org/learn/convolutional-neural-networks
This course has a good explanation on yolo(There assignment is on car detection as well which can easily be extended to car counting) and the rest of the course is quite nice as well
2)https://towardsdatascience.com/yolo-you-only-look-once-real-time-object-detection-explained-492dc9230006
The article focus on a few implementation details and talks about the papers yolo and yolov2 , and helped me clear a out a few issues i had when i was trying to implement yolo
3)The original paper (although this may be too advanced ): https://arxiv.org/pdf/1506.02640v5.pdf
4)A keras implmentation : https://github.com/experiencor/keras-yolo2
A simple git clone if you wish to simply have the code , although i do not recommend this as it has very little actual learning and is simply a download and use option
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I am trying to do a paper about the Machine learning been applied in NLP. Can you guys please suggest me applications that have already used the Machine learning with the NLP?
The list is broad since machine learning is becoming more and more mainstream.
Regarding text, images and video, a good list of APIs would be:
AT&T Speech, IBM Watson, Google Prediction, Wit.ai, AlchemyAPI, Diffbot and I guess Project Oxford as well.
Hope it helps.
If you want something generic you can use this tutorial: http://www.cs.columbia.edu/~mcollins/papers/tutorial_colt.pdf
It is probably not the more recent information but you could find it useful if you start to learn ML methods for NLP.
As it is mentionned in this tutorial, ML methods are generally linked to the NLP task (Information Extraction, Machine Translation, etc.).
IBM Watson project is an example of platform that uses NLP and ML.
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Disclaimer: although I know some things about big data and am currently learning some other things about machine learning, the specific area that I wish to study is vague, or at least appears vague to me now. I'll do my best to describe it, but this question could still be categorised as too vague or not really a question. Hopefully, I'll be able to reword it more precisely once I get a reaction.
So,
I have some experience with Hadoop and the Hadoop stack (gained via using CDH), and I'm reading a book about Mahout, which is a collection of machine learning libraries. I also think I know enough statistics to be able to comprehend the math behind the machine learning algorithms, and I have some experience with R.
My ultimate goal is making a setup that would make trading predictions and deal with financial data in real time.
I wonder if there're any materials that I can further read to help me understand ways of managing that problem; books, video tutorials and exercises with example datasets are all welcome.
Take ML course on coursera. It is a good introductery into ML algorithms which will tell you what ML could do\some general approaches:
https://www.coursera.org/course/ml
Also to get a broader picture I suggest coursera's DataSciense course:
https://www.coursera.org/course/datasci
Finally a good book is Mahout in action - it is more about solving practical matters with mahout and has lots of examples and case-studies.
I beleive after that you will have a better understanding of what you want to do next.
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I have found OpenCV code that uses CvGaussBGStatModelParams structure, cvCreateGaussianBGModel, and other related functions. However, I haven't been able to find any explanations for how they work and how they are to be used and what they mean.
Any help would be appreciated.
These functions are undocumented (at least as far as the manual goes). However, if you look around in the source, you will find them in src/cvaux/cvbgfg_gaussmix.cpp. In there:
This is based on the "An Improved
Adaptive Background Mixture Model for
Real-time Tracking with Shadow
Detection" by P. KaewTraKulPong and R.
Bowden
http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
The windowing method is used, but not
the shadow detection. I make some of
my own modifications which make more
sense. There are some errors in some
of their equations.
That link is probably a good start for you.