Module 3: Advanced Deep Learning Techniques (TD-M3-ADLT)

Course agenda:

  1. Working out the data (days 1-2):
    1. Establishing schema;
    2. Dealing with missing values;
    3. Detecting anomalies w.r.t. reference data;
    4. Versioning data;
    5. Gathering data:
      1. Concept of data distribution;
      2. Deciding further collection based on baseline performance;
      3. Active learning.
    6. Succeeding with small data:
      1. Pre-trained models;
      2. Transfer learning;
      3. Representation learning;
      4. Data augmentation.
  2. Advanced deep learning methods (days 2-3):
    1. Model calibration;
    2. Self-supervised learning;
    3. Knowledge-distillation;
    4. Network pruning;
    5. Adversarial training.
  3. Advanced deep learning architectures (day 3):
    1. Attention-based models;
    2. Autoencoders;
    3. Diffusion models.
  4. Dealing with hardware requirements via model compression (day 4).
  5. Training pipelines: modularization, reproducibility, best practices (days 4-5)...
  6. Best practices for successful deployments (day 5).

Target audience:

AI Engineers, AI R&D, Data Scientists (~1-3 years of experience).


  1. Basics of Artificial Intelligence.
  2. Computer Vision and Advanced Image Processing.

What the student will know after finishing the course:

  • A data-centric approach to developing models for real use cases.
  • Fundamentals of several advanced deep learning architectures.
  • Practical application of several deep learning techniques.
  • Robust model logging and versioning.
  • How to deal with hardware constraints.
  • How to modularize code for training via training pipelines.