Topics: The following topics are covered in this module:

1. Neural networks and capabilities
2. Deep learning
3. Types of networks
4. Python AI frameworks

Objectives

By the end of this module, you will be able to:

1. Define a neural network.
2. Describe how a neural network works.
3. Discuss deep networks.
4. Discuss what can be done with neural networks.
5. Use a deep learning pre-trained model to classify an image.
6. Discuss Python AI Frameworks.

Watch

Video: Deep Learning Foundations Intro Video

Topics

• What are Neural Networks?
• What is Deep Learning?
• What can I do with Neural networks?
• Types of Networks
• Python AI Frameworks

Exercise

Now that we’ve thoroughly explored the basics of neural networks, let’s get our hands dirty with creating one ourselves. The following exercise will use the Python coding language inside a Jupyter Notebook environment to give you a quick introduction to doing AI with code!

The notebooks for the Deep Learning Foundations course are located at the deep learning directory on our Practicum AI GitHub.

• Click the “Use this template” button to make your copy of the repository.
  • If you need details on how to do this, please see this page on creating your GitHub repository from the template.
• Open a session on HiPerGator, Google Colab, or your HPC (we will want a GPU for these exercises).
  • If you need details for this step, please view the appropriate page:
    • Launching Jupyter on HiPerGator.
    • Launching a Google Colab session.
• Work through notebook 01_deep_learning_tour.ipynb.

• Stuck or need to see this in action? Watch the Deep Learning Tour Walkthrough video:

  • Video: Deep Learning Tour Walkthrough video (28:02)