Learning Outcome

What knowledge and skills will you acquire by the end of this course?

  • Understand modern Deep Learning technology

  • Implement image classification AI models with Python

  • Set up infrastructure required for AI model development

  • Understand pros and cons of various deep learning frameworks

Course curriculum

  1. 1

    • 1 Introduction to AI

    • 2 AI infrastructure management

    • 2 Links

    • 3 Linux for AI

    • 3 Links

    • 4 Python for AI

    • 4 img_view.py

    • 5 Introduction to TensorFlow

    • 5 Conda environments manual

    • 5 t1.py

    • 5 exercise.py

    • 6 Introduction to deep learning

    • 7 Introduction to Keras

    • 7 Data

    • 7 validation.txt

    • 7 Final code

    • 8 Image classification 1

    • 8 mnist_classification.py

    • 8 mnist_classification_tensorflow2.py

    • 9 Image classification 2

    • 9 cifar10_classification_NN.py

    • 9 cifar10_classification_CNN.py

    • 9 cifar10_classification_DCNN.py

    • 10 Image classification 3

    • 10 Links

    • 10 caltech101_classification.py

    • 11 Introduction to computer vision

    • 12 Introduction to PyTorch

    • 12 Installing PyTorch

    • 12 cifar10_pytorch.py

FAQ

  • I don't have experience in programming. Can I take this course?

    We strongly advise you to have basic knowledge of Python programming before taking this course. You can take our Introduction to Python course!

  • Do I need to know about AI to take this course?

    Don't worry. We will teach you from what AI is. No need for experience or knowledge in AI.

  • What kind of hands-on experience do you offer?

    In this course, you will build a few Image Classification models using Keras and PyTorch, which are common frameworks used by a lot of industries.

  • I don't have any Deep Learning frameworks installed. Do I need to?

    We will walk you through to process of installing everything you need and setting up the environment. So don't worry!