Unofficial pages of Deep Learning 1 at UniZg-FER

About the course

Deep learning is a branch of machine learning that is particularly suitable for solving non-linear recognition problems in the field of artificial intelligence. Deep learning maps input data into complex representations through a composition of learned nonlinear transformations. These methods find their application in challenging tasks where the dimensionality of data is extremely large: computer vision, natural language processing or speech comprehension. This course introduces the most important discriminative approaches with special emphasis on practical implementations.

Lectures

Laboratory exercises

  1. Logistic regression, gradient descent, Python, numpy: instructions.
  2. PyTorch, fully connected models, MNIST: instructions;
  3. Convolutional models, MNIST, CIFAR: instructions;
  4. Recurrent models: instructions.
  5. Metric embeddings: instructions.

Literature

  1. Ian Goodfellow, Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press (html)
  2. Aston Zhang, Zack C. Lipton, Mu Li, Alex J. Smola. Dive into Deep Learning. (html)
  3. Michael Nielsen. Neural Networks and Deep Learning. Determination press. (html)

Exams

A successful completion of the lab exercises is a prerequisite for taking the exam. Please send us an e-mail as soon as you complete any of the lab exercises in order to take the lab assessment.

Our exams typically consist of 12 short theoretical questions (you need to choose a corect answer, 30% points) and 5-6 problems (70% points). To get a feeling on how the problems might look like, please have a look at our previous exams listed below.

  1. Exercises with solutions by Marin Kačan 2024/25 (pdf)
  2. Mid-term exam 2023/24 (pdf)
  3. Final exam 2016/17 (txt)

Student projects

Interesting links