Materials

As you follow along this lesson, it's extremely important that you open the Jupyter notebook and attempt the exercises. Much of the value in this experience will come from seeing how your solution is different from Andrew's and playing around with the code in your own way. Make this lesson count!

Workspace

The best way to open the notebook is to click here, which will open it in a new window. We recommend you to work on the notebook in that window, and watch the videos in this one. You can also get to the notebook by clicking the "Next" button in the classroom.

If you want to download the notebooks yourself, you can clone them from our GitHub repository. You can either download the repository with git clone https://github.com/udacity/deep-learning.git, or download it as an archive file from this link.

This lesson uses the following files:

• Sentiment_Classification_Projects.ipynb - a notebook you will use to following along and work on lesson mini projects.
• Sentiment_Classification_Solutions.ipynb - a notebook that includes Andrew’s solutions to the lesson projects, which you can use for reference
• A notebook for the solution for each mini project.
• reviews.txt - a collection of 25 thousand movie reviews
• labels.txt - positive/negative sentiment labels for the associated reviews in reviews.txt

Note: the notebooks for these lessons have been updated since the videos were recorded. In most cases that just means your notebook will contain more hints and explanatory text than what you see in the videos, but there may be some minor differences in the code as well. With these changes, you still will be able to follow along with the lessons, and should have an easier time understanding the project material.

Solutions

If you need help, feel free to look at the solutions in the same folder.