Kristofer Koishigawa 135928dc5d
fix(learn): update machine learning project colab links (#40213)
* fix(learn): update machine learning project colab links

Update current machine learning project Colab links to clone Jupyter notebooks from the boilerplate repos rather than the ones in Google Drive.

* fix(learn): update chinese versions of colab links
2020-11-11 08:56:23 -08:00

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---
id: 5e46f8dcac417301a38fb92e
title: Cat and Dog Image Classifier
challengeType: 10
---
## Description
<section id='description'>
For this challenge, you will use TensorFlow 2.0 and Keras to create a convolutional neural network that correctly classifies images of cats and dogs with at least 63% accuracy.
You can access <a href='https://colab.research.google.com/github/freeCodeCamp/boilerplate-cat-and-dog-image-classifier/blob/master/fcc_cat_dog.ipynb' target='_blank'>the full project instructions and starter code on Google Colaboratory</a>.
After going to that link, create a copy of the notebook either in your own account or locally. Once you complete the project and it passes the test (included at that link), submit your project link below. If you are submitting a Google Colaboratory link, make sure to turn on link sharing for "anyone with the link."
We are still developing the interactive instructional content for the machine learning curriculum. For now, you can go through the video challenges in this certification. You may also have to seek out additional learning resources, similar to what you would do when working on a real-world project.
</section>
## Instructions
<section id='instructions'>
</section>
## Tests
<section id='tests'>
```yml
tests:
- text: 'It should pass all Python tests.'
testString: ''
```
</section>
## Challenge Seed
<section id='challengeSeed'>
</section>
## Solution
<section id='solution'>
```py
# Python challenges don't need solutions,
# because they would need to be tested against a full working project.
# Please check our contributing guidelines to learn more.
```
</section>