Could anyone give me an Example in detail which show how SVM exactly work with all the necessary Mathematics?
As I tried to search in the internet and found very little example about SVM.
Thank you.
You can watch the stanford lectures on machine learning from lectures 6 to 8. Its on youtube. It teaches you everything on SVM and the mathematics involved in SVM. Have a look at it..
Related
I'm sorry, I'm just starting to learn NLP. I am learning how to use Google Flan.
I'm not sure how to provide the few-shot example to the model and run which file to get the results.
Here is the code link:
https://github.com/google-research/FLAN
Here is the paper:
https://arxiv.org/abs/2109.01652
Many thanks!
Good day, I am a student that is interested in NLP. I have come across the demo on AllenNLP's homepage, which stated that:
The model is a simple LSTM using GloVe embeddings that is trained on the binary classification setting of the Stanford Sentiment Treebank. It achieves about 87% accuracy on the test set.
Is there any reference to the sample code or any tutorial that I can follow to replicate this result, so that I can learn more about this subject? I am trying to obtain a Regression Output (Instead of classification).
I hope that someone can point me in the right direction.. Any help is much appreciated. Thank you!
AllenAI provides all code for examples and lib opensource on Git, including AllenNLP.
I found exactly how the example was run here: https://github.com/allenai/allennlp/blob/master/allennlp/tests/data/dataset_readers/stanford_sentiment_tree_bank_test.py
However, to make it a Regression task, you'll have to tweak directly on Pytorch, which is the underlying technology for AllenNLP.
I am doing a License Plate Recognition system using python. I browsed through the net and I found many people have done the recognition of characters in the license plate using kNN algorithm.
Can anyone explain how we predict the characters in the License Plate using kNN ?
Is there any other algorithm or method that can do the prediction better ?
I am referring to this Git repo https://github.com/MicrocontrollersAndMore/OpenCV_3_License_Plate_Recognition_Python
Well, I did this 5 years ago. I will suggest you that, maybe right now is so much better to do this using ML Classifier Models, but if you want to use OpenCV. OpenCV has a pretty cool way to make ANPR using an OCR.
When I did it, I used a RasberryPi for processing and capture images and with c++ run openCV in another computer. I recommend you check this repo and if you're interested look for the book reference there. I hope my answer helps you to find your solution.
https://github.com/MasteringOpenCV/code.
How to use the Inception V3 tensorflow module to train with our own requirement dataset images. Say for example I want to train the Inception V3 module with the different cool drinkcompany brands Pepsi, Sprite etc.. How it can be achieved..??
In the link https://github.com/tensorflow/models/tree/master/inception they have explained with the ImageNet. I am bit confused with that. Please explain the stuff.
I suggest you to check Transfer Learning. which consists in retrain only the last layers with new categories
How to Retrain Inception's Final Layer for New Categories
Baptiste's answer linking to the Tensorflow site is good. This is a very broad question and his link is a good start.
If you'd like something a little more step-by-step then the Tensorflow for Poets tutorial is basically the same but doesn't require the use of Bazel commands. It initially uses flowers but you can use whatever dataset you want.
There are many other examples and tutorials on the web. I found some more with a quick search including this page and this video.
Good Luck!
I am learning to implement a hand gesture recognition project. For this, I have gone through several tutorials where they use color information, background subtraction, various object segmentation techniques.
However, one that I would like to use is a method using cascading classifiers however I dont have much understanding in this approach. I have read several text and papers and I understand its theory however, I still dont understand what are good images to train the cascading classifer on. Is it better to train it on natural color images or images with hand gestures processed with canny edge detection or some other way.
Also, is there any method that uses online training and testing methods similar to openTLD but where the steps are explained. The openCV documentation for 2.3-2.4.3 are incomplete with respect to the machine learning and object recognition and tracking except for the code available at: http://docs.opencv.org/doc/tutorials/objdetect/cascade_classifier/cascade_classifier.html
I know this is a long question but I wanted to explain my problem thoroughly. It would help me to understand the concept better than just to use online code.
Sincere thanks in advance!
if you think about haar classifier, a good tutorial is here