Set up TensorFlow and Google Drive integration seamlessly to streamline your neural network workflow. Learn how to efficiently prepare your environment for data-driven projects using Jupyter Notebook and TensorFlow.
Key Insights
- Import TensorFlow as the sole required library for establishing and managing neural networks within a Jupyter Notebook environment.
- Configure a connection to Google Drive directly within the Jupyter Notebook to ensure easy access and management of datasets and working files.
- Initial setup, specifically importing TensorFlow, may require additional processing time, especially during the first execution due to the library's significant size.
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Let's do a brief setup here. We are going to import our standard data libraries and Jupyter Notebook libraries. We're going to set up our Google Colab, sorry, rather our Google Colab connection to Google Drive.
We're going to import TensorFlow. That's the only library we'll need to handle this data, to handle setting up a neural network. And we'll set up our base URL for all the work we'll do.
Where is it in our Google Drive? So I'm going to run the this and all above cell blocks. Now, if you are running this for the first time in this, of course, it'll take an extra moment to set everything up. It's going to take a while to import TensorFlow, which is pretty big.
I'll run this, make sure I have my base URL. And of course, if you're running this for the first time in this, it'll also prompt you to connect Google Drive to this notebook. All right, that's it for setup.
Next, we'll dive into the data itself.