I'm a Kaggle user. I think this is a very interesting game for data scientist. I want to build a system like Kaggle. Does anybody have any Idea how to do this?
Thank you very much!
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I'm working on a project that requires NLP. The scenario involves two or more participants exchanging messages in a chat app. I want to use am NLP/ML model to auto-tag message for the participants' emotions as the conversation is happening (e.g happy, sad, anger, frustrated, I'm not looking for sentiment analysis). My prior knowledge of NLP is limited, and I had a hard time finding the right model to use.
I found this repo conv-emotion and spent almost two days trying to make it work. Currently reaching out to the authors for help. I like their repo is because it's the model is applied on a conversation level. But unfortunately, their README isn't well written.
Any suggestions going forward? Do you know any model or even API that I can use? Am I on the right track?
For sentiment analysis, I think to go for Gated CNN will get you quite a good result. And pair it with some rules and you'd be fine. Try reading this paper : https://www.aclweb.org/anthology/P18-1234/
I am new in Machine Learning and Deep Learning.
I am working on one use-case if any one can help pls.
We have employee attendance dataset i want to predict the employee in Time for the future days.
What algorithm i can use that will best fit to my problem. need some guidance how i can proceed.
this is basic machine learning, if you looked it up just a bit, you would have found a solution, with that being said, you could use linear regression
I want to train a new haar-cascade for glasses as I'm not satisfied with the results I'm getting from the cascade that is included in OpenCV.
My main problem is that I'm not sure where to get eyeglasses images. I can manually search and download, but that's not practical for the amount of images I really need. I'm specifically looking for images of people wearing eyeglasses.
As this forum contain many experienced computer vision experts, I hope someone here can guide as to how to obtain images for training.
I'll also be happy to hear other approaches for detecting eyeglasses (on people).
Thanks in advance,
Gil
If you simply want images, it looks like #herhuyongtao pointed you to a good place. Then you can follow opencv's tutorial on training.
Another option is to see what others have trained:
There's a trained data set found here that might be of use, which states simply that it is "better". I'm assuming that it's supposed to be better than opencv.
I didn't immediately see any other places for trained or labeled data.
I'm doing some research on machine learning algorithms that would be useful for processing image data and using them for recognition purposes. I've stumbled across SpikeNET and thought it had potential. However their example code is very confusing (the comments are in French) and being on a Windows box I cannot compile the project without fiddling around in Cygwin too much.
If anyone has any further information on the Spiking Neuron technology or any other highly researched machine learning techniques that yield good results, I would be highly interested.
Thanks in advance.
Well, for object detection the more "standard" state of art approaches are haar cascades and SIFT features.
As for "working code" have you spent any time at all poking around OpenCV? this is a very complete computer vision library that can help you along the way. Perhaps start here?
hello
I have to develop a software for my college course that will perform retinal scan, i.e, if a picture is provided the location of the retina will be detected by the program.
But i have got no clue on how to implement this project. Can anyone please provide any relevant information?
I would perhaps start out researching how a face detection algorithm is implemented, and then implement that same algorithm with an iris as the target.
Heres an open source Java implementation of a face detection algorithm: Here
What is the college course? Hopefully you're given more guidance (or should already have knowledge in the area) beyond "develop an algorithm to find the retina/iris". It could probably be done with shape recognition, or various other techniques depending on what the image is like. Are we talking about "Here's a closeup of a face, find the eyes", or "Here's a picture of 10 people, find the eyes"? The algorithms will be very different in those two cases.