Overview

Duration is 1 min

In this notebook, we will implement text models to recognize the probable source (Github, Tech-Crunch, or The New-York Times) of the titles we have in the title dataset we constructed a related AutoML processed lab.

Learning objectives

In this lab, you will:

• Learn how to tokenize and integerize a corpus of text for training in Keras
• Learn how to do one-hot-encodings in Keras
• Learn how to use embedding layers to represent words in Keras
• Learn about the bag-of-word representation for sentences
• Learn how to use DNN/CNN/RNN model to classify text in keras

Setup and requirements

For each lab, you get a new Google Cloud project and set of resources for a fixed time at no cost.

1. Sign in to Google Skills using an incognito window.

2. Note the lab's access time (for example, 1:15:00), and make sure you can finish within that time.
   There is no pause feature. You can restart if needed, but you have to start at the beginning.

3. When ready, click Start lab.

4. Note your lab credentials (Username and Password). You will use them to sign in to the Google Cloud Console.

5. Click Open Google Console.

6. Click Use another account and copy/paste credentials for this lab into the prompts.
   If you use other credentials, you'll receive errors or incur charges.

7. Accept the terms and skip the recovery resource page.

Task 1. Launch Vertex AI Workbench instance

1. In the Google Cloud console, from the Navigation menu (), select Vertex AI > Dashboard.

2. Click Enable All Recommended APIs.

3. In the Navigation menu, click Workbench.

   At the top of the Workbench page, ensure you are in the Instances view.

4. Click Create New.

5. Configure the Instance:

   • Name: lab-workbench
   • Region: Set the region to
   • Zone: Set the zone to
   • Advanced Options (Optional): If needed, click "Advanced Options" for further customization (e.g., machine type, disk size).

6. Click Create.

This will take a few minutes to create the instance. A green checkmark will appear next to its name when it's ready.

7. Click Open Jupyterlab next to the instance name to launch the JupyterLab interface. This will open a new tab in your browser.

8. Click the Python 3 icon to launch a new Python notebook.

9. Right-click on the Untitled.ipynb file in the menu bar and select Rename Notebook to give it a meaningful name.

Your environment is set up. You are now ready to start working with your Vertex AI Workbench notebook.

Click Check my progress to verify the objective. Launch Vertex AI Workbench instance

Task 2. Clone course repo within your Vertex AI Workbench instance

The GitHub repo contains both the lab file and solutions files for the course.

1. Copy and run the following code in the first cell of your notebook to clone the training-data-analyst repository.

2. Confirm that you have cloned the repository. Double-click on the training-data-analyst directory and ensure that you can see its contents.

Click Check my progress to verify the objective. Clone course repo within your Vertex AI Workbench instance

Task 3. Keras for text classification using Vertex AI

Duration is 60 min

1. In the notebook interface, navigate to training-data-analyst > courses > machine_learning > deepdive2 > text_classification > labs > keras_for_text_classification.ipynb.

2. In the Select Kernel dialog, choose TensorFlow 2-11 (Local) from the list of available kernels.

3. In the notebook interface, click on Edit > Clear All Outputs.

4. Carefully read through the notebook instructions and fill in lines marked with #TODO where you need to complete the code.

Tip: To run the current cell, click the cell and press SHIFT+ENTER. Other cell commands are listed in the notebook UI under Run.

• Hints may also be provided for the tasks to guide you along. Highlight the text to read the hints (they are in white text).
• To see the complete solution, navigate to training-data-analyst > courses > machine_learning > deepdive2 > text_classification > solutions and open keras_for_text_classification.ipynb.

End your lab

When you have completed your lab, click End Lab. Google Skills removes the resources you’ve used and cleans the account for you.

You will be given an opportunity to rate the lab experience. Select the applicable number of stars, type a comment, and then click Submit.

The number of stars indicates the following:

• 1 star = Very dissatisfied
• 2 stars = Dissatisfied
• 3 stars = Neutral
• 4 stars = Satisfied
• 5 stars = Very satisfied

You can close the dialog box if you don't want to provide feedback.

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