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Learning Objectives

After working with this framework, students will be able to:

Core Understanding​

  • Understand the fundamental architecture of deep learning frameworks
  • Master the principles of automatic differentiation
  • Comprehend how tensors and computational graphs work
  • Learn the implementation details of basic neural network operations

Framework Components​

  • Implement and understand various loss functions
  • Design and implement optimization algorithms
  • Build neural network layers from scratch
  • Grasp the relationship between different framework components

Advanced Concepts​

  • Understand GPU acceleration in deep learning (coming soon)
  • Learn about model deployment through ONNX export
  • Explore the balance between performance and usability
  • Recognize design patterns in deep learning frameworks

Practical Skills​

  • Debug deep learning code effectively
  • Implement custom operations and layers
  • Write clean, maintainable deep learning code
  • Transition smoothly to industrial frameworks like PyTorch

Development Practices​

  • Follow good software engineering practices in ML development
  • Write clear documentation for ML code
  • Understand testing strategies for ML systems
  • Manage project complexity in deep learning applications