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Module 1 introduces the world of machine learning (ML) and its powerful role in image and video processing. Machine learning allows computers to learn from examples while also following hardcoded instructions. Like a human learning through practice, a machine gets better at tasks by going through cycles of training. The video provided below gives an excellent overview of image processing with machine learning, and allows for a smooth visual introduction for participants into the subject.
Module 1 Introduction will help you:
Module 1 Introduction will help you:
Explore using Google Colab to run pre-written Python code.
Explore introductory image processing and machine learning concepts.
Explore using a practical, real-world application: training a computer to tell the difference between cats and dogs using a pre-trained ResNet50 model.
Required materials/resources:
Required materials/resources:
Access to a computer and a large screen (if you want to share with others)
A Google account to access Google Colab
Approximately 1 GB of available storage for downloading and importing the image dataset into Google Colab
Keywords:
Keywords:
Colab Notebook or Notebook - When we talk about opening or using a Colab Notebook, we are referring to a document in which both text and code can be shown and experienced together. There is much more to Google Colab notebooks, but the website mainly focuses on these aspects.
Script - A script, in Python, is one or more lines of code written to be executed together in a sequential order. This activity's Colab notebook has two scripts within it: the "I am ready!" script and the "Cat and Dog Classification" script. Future modules may have more or less scripts within them, depending on what is being conveyed.
Output Window - The output window in a Colab notebook appears below each executable script. If a script's code creates a visual output, it will appear in the output window. Additionally, if a script encounters any errors or exceptions, it will print these errors in the output window. In the case of the "*I am ready!*" script in this activity's Colab notebook, the output, "I am ready!" is printed in the output window after execution.
Activity Instructions
Activity Instructions
- Preparation
Read through the Google Colab Interaction Guide and follow along to ensure you have the Image Processing Image Dataset correctly uploaded to your Google Drive.
- Activity
Read through the Introduction to Module 1 Colab Notebook.
Run the code located at the bottom of the Colab notebook and test it with your own pet's image or any image not obtained from the dataset.
Upload your test image to your Google Drive inside the uploaded Image Processing Image Dataset folder. Pay close attention to the naming convention you use, ensure you correctly name your test image or temporarily change the test image path destination in the Google Colab code.
Downloadable Content & Links
Downloadable Content & Links
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