I have this image that contains text (numbers and alphabets) in it. I want to get the location of all the text and numbers present in this image. Also I want to
If you have both a classification and regression problem that are related and rely on the same input data, is it possible to successfully architect a neural net
My question is about coding a neural network which does regression (and NOT classification) using tflearn. Dataset: fixed acidity volatile acidity citric acid
I am confused since google cannnot train their text generation models with each individuals personal vocabulary. I was trying to develop something similar but
I am implementing a CNN for an highly unbalanced classification problem and I would like to implement custum metrics in tensorflow to use the Select Best Model
I implemented a univariate xgboost time series using the following code, def series_to_supervised(data, n_in=1, n_out=1, dropnan=True): n_vars = 1 if type(d
I'm trying to train a model for a text classification and the model take a list of maximum 300 integer embedded from articles. The model trains without problem
I have a set of training data that consists of X, which is a set of n columns of data (features), and Y, which is one column of target variable. I am trying to
I'm developing a device for Freshwater Quality Management which can be used for freshwater bodies such as lakes and rivers. The project is spr
The Paper regarding die shap package gives a formula for the Shapley Values in (4) and for SHAP values apparently in (8) Still I don't really understand the dif
It is common practice to augment data (add samples programmatically, such as random crops, etc. in the case of a dataset consisting of images) on both training
So I'm using the gym stocks environment to train a model using A2C policy but I want to understand how the profit is calculated by the model, in the documentati
I am working on the toy dataset with ColumnTransformer and pipeline but I came across the error which I couldn't find a solution on the internet. toy = pd.read_
I have a task to calculate inter-annotator agreement in multi-label classification, where for each example more than one label can be assigned. I found that NLT
I have read that LBP can be used for rotation invariant feature detection, such as here. This makes intuitive sense to me, as LBP is effectively evaluating loca
I am using keras+tensorflow for the first time. I would like to specify the correlation coefficient as the loss function. It makes sense to square it so that it
I am trying to get a Tensorflow TFX pipeline up and running using the MNIST dataset. # Imports import pandas as pd import numpy as np from keras.datasets import
I use Python pycaret module to analyze big set of data. I did setup, compare_model, create_model correctly, but when I try to use model I created to predict the
samples.zip The sample zipped folder contains: model.pkl x_test.csv To reproduce the problems, do the following steps: use lin2 =joblib.load('model.pkl') to loa
samples.zip The sample zipped folder contains: model.pkl x_test.csv To reproduce the problems, do the following steps: use lin2 =joblib.load('model.pkl') to loa