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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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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 was thinking if there is a pre-built implementation of MLP in python that can take my objective function, loss function, and tolerance as input and return the optimum parameters for my function. I have gone through the MLPs in Tensorflow and scikit-learn but there seems to be nothing of this sort. Any suggestions are welcome.
Thanks in Advance
As long as your objective function is differentiable this is literally what a neural network is designed to do. You can write any function in TF as an objective, and then train your MLP with say SGD. It is a matter of understanding how things work or accepting that "pre built" is not going to be as easy as a function called "solve my problem", it requires a few more commands, but in the end what you are asking for is literally any NN implementation, let it be TF, Keras etc.
For example you can use Keras and implement your custom loss
def my_loss_fn(y_true, y_pred):
squared_difference = tf.square(y_true - y_pred)
return tf.reduce_mean(squared_difference, axis=-1) # Note the `axis=-1`
model.compile(optimizer='adam', loss=my_loss_fn)
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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'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.
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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