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It would appear that they have deleted the data source by mistake. No attached data sources. (Currently they have just one Kaggle notebook that works. They may be re-uploading the notebook at some point.)
One of the more popular ways to run machine learning code is via the Kaggle Kernels. These kernels are based on Python code that can be uploaded and run online on a Kaggle account. As of February 17th 2020, there are over 10,000 Kaggle notebooks uploaded to the Kaggle Kernels GitHub project. In this tutorial, we’ll use Kaggle Kernels to run code on a simple MNIST dataset.
This tutorial is written for users with very little Python or machine learning experience. While a variety of tutorials exist for working with different kinds of data, we’ll focus on basic, hands-on, learning. We will use the Kaggle Python API to query Kaggle Kernels, run our code, and collect output data to view using Cloud Datalab.
MNIST Dataset
To run code on the Kaggle Kernels, we’ll use a simple dataset: the MNIST dataset.
The MNIST dataset is one of the most widely used image datasets for machine learning. It is a commonly used test bed for machine learning and natural language processing algorithms.
Getting MNIST
We’ll start by downloading the MNIST dataset. The dataset is made available to the public by Paul Szegedy, the author of the MNIST tutorial and one of the developers of the Kaggle Kernels.
We’ll first need to visit the Kaggle Kernels GitHub page. To do this, open a web browser and visit kagglekernels.github.io. At the top of the page, select the Github repository of the Kaggle kernels you want to query. For this tutorial, we will select the following GitHub repository: kagglekernels/samples/mnist
You should be presented with a page with a list of the code repositories on this repository. Scroll down to the very bottom and click on the Open in Cloud Datalab button. You will be redirected to Cloud Datalab.
Cloud Datalab
Cloud Datalab is a suite of tools for querying, visualizing, and running data science workflows on notebooks in Google 3125841983
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