Unofficial pages of the Deep Learning course at UniZgFER
About the course
Lectures
Currently we only offer lectures in Croatian.
We cover the following subjects:

required previous knowledge (chapters 25),

fully connected models (chapter 6)

convolutional models (chapter 9, sections 15),

optimization (chapter 8, sections 15),

regularization (chapter 7, sections 110),

recurrent models (chapter 10, sections 15, 7, 1011),

generative models (chapter 20, sections 13).
The chapters and sections refer to the textbook by Goodfellow et al.
Literature

Ian Goodfellow, Yoshua Bengio and Aaron Courville.
Deep Learning.
MIT Press
(html)

Aston Zhang, Zack C. Lipton, Mu Li, Alex J. Smola.
Dive into Deep Learning.
(html)

Michael Nielsen.
Neural Networks and Deep Learning.
Determination press.
(html)
Exams
Currently, the English version of this course can be completed
only through a comprehensive exam in June/July.
Please note that successful completion of the lab exercises
is a prerequisite for taking the exam.
Please send us an email 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 56 problems (70% points).
To get a feeling on how the problems might look like,
please have a look at our previous exams listed below.
These documents are in Croatian,
however Google translate
does a pretty good job these days,
so I think they might be helpful.

Midleterm exam 2016/17
(pdf)

Final exam 2016/17
(txt)
Laboratory exercises

Logistic regression, gradient descent, Python, numpy:
instructions.

Tensorflow, fully connected models, MNIST:
instructions;

Convolutional models, MNIST, CIFAR:
instructions;

Recurrent models:
instructions.

Generative models:
instructions.
Student projects

A minimal framework for reversemode automatic differentiation in Python (symboltosymbol).
Bruno Gavranović.
Seminar,
slides,
code.
Interesting links

Yann LeCun, Yoshua Bengio, Geoffrey Hinton.
Deep learning.
pdf

Pedro Domingos.
A Few Useful Things to Know about Machine Learning.
CACM 2012.
pdf

Awesome Deep Vision.
A curated list of deep learning resources
for computer vision.
html

Tensorflow  open source deep learning framework.
html

A neural network playground.
html

C. Olah.
Neural networks and data representations.
html

Convolutional Neural Networks for Visual Recognition.
Stanford CS programme.
html

Deep Learning for Natural Language Processing.
Stanford CS programme.
html

Deep Learning Courses.
html

Terence Tao.
Linear algebra.
html

Randal Barnes.
Matrix Calculus.
html

Eduardo Sontag.
VC Dimension of Neural Networks. NATO ASI Series F.
pdf