Ishitori commented on a change in pull request #11651: Add logistic regression tutorial URL: https://github.com/apache/incubator-mxnet/pull/11651#discussion_r203197993
########## File path: docs/tutorials/gluon/logistic_regression_explained.md ########## @@ -0,0 +1,215 @@ + +# Logistic regression using Gluon API explained + +Logistic Regression is one of the first models newcomers to Deep Learning are implementing. In this tutorial I am going to focus on how to do logistic regression using Gluon API and provide some high level tips. + +Before anything else, let's import required packages for this tutorial. + + +```python +import numpy as np +import mxnet as mx +from mxnet import nd, autograd, gluon +from mxnet.gluon import nn, Trainer +from mxnet.gluon.data import DataLoader, ArrayDataset + +mx.random.seed(12345) # Added for reproducibility +``` + +In this tutorial we will use fake dataset, which contains 10 features drawn from a normal distribution with mean equals to 0 and standard deviation equals to 1, and a class label, which can be either 0 or 1. The length of the dataset is an arbitrary value. The function below helps us to generate a dataset. Review comment: I didn't want to add extra code for data loading and data processing, because this is not the point of this tutorial. The optimal way would be if there is a binary classification dataset in the mxnet itself, so it can be loaded in one line and no pre-processing would be required. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] With regards, Apache Git Services
