Closed. This question does not meet Stack Overflow guidelines. It is not currently accepting answers.
We don’t allow questions seeking recommendations for books, tools, software libraries, and more. You can edit the question so it can be answered with facts and citations.
Closed 6 years ago.
Improve this question
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.
Related
Closed. This question does not meet Stack Overflow guidelines. It is not currently accepting answers.
We don’t allow questions seeking recommendations for books, tools, software libraries, and more. You can edit the question so it can be answered with facts and citations.
Closed 4 months ago.
Improve this question
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
Closed. This question does not meet Stack Overflow guidelines. It is not currently accepting answers.
We don’t allow questions seeking recommendations for books, tools, software libraries, and more. You can edit the question so it can be answered with facts and citations.
Closed 4 years ago.
Improve this question
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
Closed. This question does not meet Stack Overflow guidelines. It is not currently accepting answers.
We don’t allow questions seeking recommendations for books, tools, software libraries, and more. You can edit the question so it can be answered with facts and citations.
Closed 4 years ago.
Improve this question
Thanks for making it this far on my post!
I am studying engineering, yet have a passion for programming and wish to implement computer science knowledge into my own research.
My question is pertaining to any resources that this community has available and any advice you all are willing to give regarding getting started in this broad field.
I’m mainly confused about ‘neural networks’ in relation to Deep Learning as well as implementation of algorithms.
I have slight Python and R knowledge.
Note: one of the subfora of StackExchange is probably a better fit for this question.
In any case, for ML you can do just fine with basic Python/R. Most of the research and work done on ML is based on TensorFlow and similar frameworks currently (2018). To use the frameworks you don't really need a strong programming background to setup and train models on them (although it certainly helps). Actually, math/statistics will help you more, specially if you want to get to the bottom of it (i.e. reading the latest articles/papers, etc.).
Mainly I’m confused about ‘neural networks’ in relation to Deep Learning
"Deep Learning" is basically taking advantage of modern computing capabilities to train complex models (e.g. neural networks with many hidden layers) which a few years ago (e.g. 10 years ago) were unfeasible. Informally speaking, the more complex your network is, the more interesting are the things that it can learn.
as well as implementation of algorithms.
Typically, you will use an existing framework -- you won't implement the algorithms yourself. Although, of course, implementing a MultiLayer Perceptron by yourself is always a good and fun learning exercise.
Closed. This question does not meet Stack Overflow guidelines. It is not currently accepting answers.
We don’t allow questions seeking recommendations for books, tools, software libraries, and more. You can edit the question so it can be answered with facts and citations.
Closed 7 years ago.
Improve this question
I am keen in learning machining learning. I know programming, just want to know some useful sites which will help in understanding the concepts of machine learning with simple examples.
As a beginner in Machine Learning you should start with the book
Pattern Recognition and Machine Learning, by Christopher M. Bishop
There are some prerequisites other than programming are Linear Algebra, Probability theory, etc. i.e. you should have a strong background in Mathematics. Although the book I suggested covers the common mathematical frameworks needed for understanding Machine Learning in its introductory chapters.
Moreover, you should practice implementing different learning algorithms (start from smaller ones) to grab the concepts well. As Andrej Karpathy said,
...everything became much clearer when I started writing code.
Closed. This question does not meet Stack Overflow guidelines. It is not currently accepting answers.
We don’t allow questions seeking recommendations for books, tools, software libraries, and more. You can edit the question so it can be answered with facts and citations.
Closed 5 years ago.
Improve this question
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.