I pulled some ML code that ran on kaggle (linux) and tried to run it in a jupyter notebook on a windows machine. Here is the code (some of it): ##### RUN XGBOO
I have time series training data of about 5000 numbers. For each 100 numbers, I am trying to predict the 101st. At the end of the series, I would put in the pre
pipe = Pipeline([('reduce_dim', LinearDiscriminantAnalysis()),('classify', LogisticRegression())]) param_grid = [{'classify__penalty': ['l1', 'l2'],
while using the RandomForestRegressor I noticed something strange. To illustrate the problem, here a small example. I applied the RandomForestRegressor on a tes
I am trying to build an autoencoder with the following code import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns import
I am using dice loss for my implementation of a Fully Convolutional Network(FCN) which involves hypernetworks. The model has two inputs and one output which is
I am using dice loss for my implementation of a Fully Convolutional Network(FCN) which involves hypernetworks. The model has two inputs and one output which is
My first multiclass classication. I have values X and Y. Y have 5 values [0,1,2,3,4]. But i get this "multiclass format is not supported". Understand that i nee
I want to use default hyperparams in randomized search, how can I do it? (per_float_feature_quantization param here) grid = {'learning_rate': [0.1, 0.16, 0.2],
Can some one with expertise explain how the following vectorized format of multiple linear regression is derived from given independent variable matrix with int
I know that the input_shape for Inception V3 is (299,299,3). But in Keras it is possible to construct versions of Inception V3 that have custom input_shape if
I am having difficulties extracting misclassified images, I tried to use the following line of code: inc= np.nonzero(model.predict_classes(test_data).reshape(-
Basically, I want to plot like this: I already managed to plot the clusters using sns.scatterplot(X[:,0], X[:,1], hue=y, palette=['red', 'blue', 'purple', 'gre
I am trying to manage the results of machine learning with mlflow and hydra. So I tried to run it using the multi-run feature of hydra. I used the following cod
Below is my code for deploying my ml model on streamlit. I am using Google Collab !pip install streamlit --quiet !pip install pyngrok==4.1.1 from pyngrok import
I just read about the Keras weight initializers in here. In the documentation, only different initializers has been introduced. Such as: mode
I have some food images stored in a single folder. All the images are unlabeled, nor are they stored into separate folder such as "pasta" or "meat". My current
there! I am studying Mr. Redmon's darknet code from https://github.com/pjreddie/darknet I found the initialization of weights of a connected layer is like below
there! I am studying Mr. Redmon's darknet code from https://github.com/pjreddie/darknet I found the initialization of weights of a connected layer is like below
Below is my code: model = Sequential([ Dense(32, input_shape=(32,), activation = 'relu'), Dense(100, activation='relu'), Dense(65, input_shape=(65
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