Syllabus

Please find all the Video Lectures within this YouTube Playlist.

All lecture slides and sample codes are password protected.

  • Week 1: “Introduction to Deep Learning, networks, weights, activation functions.
  • Week 2: “Convolution operation, Convolutional Layer, and types of convolutional layers”
  • Week 3: “Layer Types, Practicum: Analysis of a Network in terms of architecture, computaion and load.”
  • Week 4: “Embedded Implementation: number of operations, memory/computation requirements, architectural optimization of DNNs.”
  • Week 5 “Precision, Quantization, analysis of weight/activation quantization, precision vs. Accuracy analyses”
  • Week 6: “Post training quantization and agressive quantization methods”
  • Week 7: “Quantization aware Training”
  • Weeks 8 & 9:
    • Systems Engineering Perspective: Concepts (computation power, memory efficiency, etc...)
    • Systems Engineering Perspective: Hardwavre platforms (CPU, GPU, TPU, FPGA, etc.)
    • Lecture Slides
  • Week 10: Binary Neural Networks
  • Week 11: Mobile Solutions for Deep Learning (codesign cont'd.)
  • Weeks 12 & 13: Neural Architectural Search
  • Week 14: Project Presentations. 
    • This week's session will be held live in Zoom. Attendance is compulsary.