Guidance on AI Development Platforms and Toolkits

Please note there are lessons with hands on practices in separate courses.

This activity will help you learn how to use various AI development platforms and toolkits. You’ll discover tools like TensorFlow, PyTorch, scikit-learn, AutoML, and even no-code/low-code options. You’ll also learn how to enhance your data and customize AI models by modifying open-source code.

Learning Objectives

• Explore AI Platforms:

  • Learn the basics of different AI development platforms such as TensorFlow, PyTorch, scikit-learn, and AutoML.
  • Understand the strengths, weaknesses, and typical use cases for each tool.

• Data Augmentation:

  • Learn techniques to expand and enhance your datasets, such as rotating, flipping, and adjusting the brightness of images.
  • See how these methods can improve your model’s performance.

• Customize AI Models:

  • Gain hands-on experience by modifying open-source code.
  • Learn to fork projects on GitHub, make changes to modules, and commit your changes to practice real-world development skills.

Example Activities

1. Toolkit Introduction Session:

   • What to Do:
     • Attend a demonstration that introduces various AI platforms.
     • Learn about each platform’s key features, pros, and cons, and see live examples of how they’re used in different projects.
   • Goal:
     • Get familiar with different tools and decide which one might be best for your project.

2. Data Augmentation Practice:

   • What to Do:
     • Work with image datasets and apply data augmentation techniques like rotation, flipping, and brightness adjustment.
     • Compare model performance before and after applying these techniques.
   • Goal:
     • Understand how modifying your dataset can help improve the accuracy and robustness of your AI model.

3. Modifying Open-Source Code:

   • What to Do:
     • Fork an open-source AI project from GitHub.
     • Make small changes to one of the modules (for example, tweak a function or adjust a parameter).
     • Commit your changes and document what you did.
   • Goal:
     • Develop practical skills in customizing AI models, giving you a taste of real-world AI development.

Key Takeaways

• Explore and Choose:
  • Get to know different AI platforms so you can choose the best tool for each project.
• Enhance Your Data:
  • Learn data augmentation techniques to improve your datasets and model performance.
• Hands-On Customization:
  • Gain experience by modifying real open-source projects, which builds your technical skills and confidence in AI development.

By engaging in these activities, you’ll develop a strong foundation in AI development tools and techniques. You’ll be better prepared to tackle real-world problems and customize AI solutions that work for you. Enjoy exploring and building with AI!

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