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I'm trying to create a multilabel classifier with scikit-learn to texts.
I am new to scikit learn and I do not know if it is possible to create a classifier for text.
My intention is to use SVM multilabel, but do not know if I have to adapt the texts to train the classifier or else you can work directly with texts.
Does anyone know some documentation on this subject?
You can refer to this example: Classification of text documents using sparse features
which can give you exposer to not only multiclass but also basic text mining details of:
Vectorizer and hashing
Feature selection
Handling Sparse Data
Comparing different basic models
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How can I train the AI models available in git Hub for predicting personality according faces?
for example:
https://github.com/AleAlfonsoHdz/predict-personality
Or - where can I find a trained AI model for predicting personality according face
All the models I saw were not trained , and I found it too complicated to train them myself...
(Preferred in Python, but other language will helpful also.)
Thank you very much!
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I am new in the NLP community and need more light on something.
I saw that Keras has an Embedding layer that is generally used before the LSTM layer. But what algorithm hides behind it? Is it Word2Vec, Glove or something else?
My task is a supervised text classification problem.
The embedding layer is a randomly initialized matrix, with the dimension of (number_of_words_in_vocab * embedding_dimension). The embedding_dimension is custom defined dimension, and an hyper-parmeter that we will have to choose.
Here, the embeddings are updated during back-propagation, and are learnt from your task and task-specific corpus.
However, pre-trained embeddings such as word2vec, glove are learnt in an unsupervised manner on huge corpus. Pre-trianed embeddings provides a good initialization for this embedding layer. Thus, you can use the pre-trained embeddings to initialize this embedding layer, and also choose if you want to freeze these emebeddings or update these embeddings during the back-propagation.
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I want to fit a model with RBF regression with regularization (ridge regression) in python. Are there python pre built functions?
Yes, in Scikit-Learn, see sklearn.linear_model.Ridge
This model solves a regression model where the loss function is the
linear least squares function and regularization is given by the
l2-norm. Also known as Ridge Regression or Tikhonov regularization.
This estimator has built-in support for multi-variate regression
(i.e., when y is a 2d-array of shape [n_samples, n_targets]).
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I am a total rookie in computer vision. I am looking to build a model without using pre-trained models for coco dataset or any open-source image datasets. Any articles or references to build such models would be appreciated. I would like to build this model from scratch and make no suggestions on pre-existing trained models or Api are irrelevant to this question. and thanks in advance for any suggestions. the programming language of preference for this project is python
How about this tutorial on keras blogs:
https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html
Should be pretty straightforward and it is written in a step by step manner by the author of Keras. It has these three stages, but you only need the first one:
training a small network from scratch (as a baseline)
using the bottleneck features of a pre-trained network
fine-tuning the top layers of a pre-trained network
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I'm doing a project these days.
Goal of this project is approximately 200 symbol recognition.
Symbols are using in the navigation(turn_right, turn_left etc..)
I'm using YOLO model now
For traning this models, I thought I needed some improvement about traning speed.
This program will using when testing new navigation.
Is there any better models?
The model needs very fast traning speed, and high accuracy
Yolo is one of the best object detection for real time detection. Fast Training and high accuracy are competing goals. Did you mean test speed (with a trained model)?
Anyway, if you need fast training I highly suggest the cyclical learning rate strategy proposed by Leslie N. Smith.
Yolo has different version, so take a look at that as well.