# Deep Learning

**Mahmood Amintoosi, Spring 2026**

Computer Science Dept, Ferdowsi University of Mashhad



In this exciting journey, we'll delve into the fascinating world of **Neural Networks** and **Deep Learning** with Python and Pytorch, where machines learn, adapt, and make decisions based on data. Topics that are covered are optmization, neural networks basics, perceptron model, mulit-layer perceptrons, convolutional neural networks, and advanced topics such as autoencoders and generative adversarial networks.

## Prerequisites

- **Programming Skills**: Familiarity with Python programming. A great starting point is [Think Python](https://allendowney.github.io/ThinkPython/), which offers a deep dive into the language's essentials.
- **Mathematics**: Basic understanding of calculus and statistics at the undergraduate level.


# Course Staff

## Instructors

[Mahmood Amintoosi](https://mamintoosi.github.io/), Email: m.amintoosi AT um.ac.ir

---

## Teaching Assistants

[Tarane Kordi](https://github.com/Taraneh-trk), TA Head


## Questions?

I will be having office hours for this course on Saturday (10:00 AM--11:30 AM). If this is not convenient, email me at m dot amintoosi AT um.ac.ir, talk to me after class or [schedule an appointment via Calendly](https://calendly.com/m-amintoosi/30min). Also you can find me at [Bale](https://web.bale.ai/chat), [Rubika](https://web.rubika.ir/), [Eitaa](https://web.eitaa.com/) and [FUM-VU](https://vu.um.ac.ir/).

<!-- ## Our Slack workspace

Come and [join our Slack](https://join.slack.com/t/fum-cs/shared_invite/zt-1zntzuw2t-JOWbsyQdGASNz~40AhWy_Q) group of Computer Science Dept, Ferdowsi University of Mashhad, to engage in course discussions. -->

---

I should mention that the original material was from [Tomas Beuzen's course](https://ubc-mds.github.io/DSCI_572_sup-learn-2). I have forked his repository and modified it to suit my own needs and preferences. I would like to thank him for his great work and generosity.

```{note}
These lectures were built using the new Sphinx-based [Jupyter Book
2.0](https://jupyterbook.org/) tool set, as part of the
[ExecutableBookProject](https://ebp.jupyterbook.org/en/latest/).  They are
intended mainly as a demonstration of these tools.
Instructions for how to build them from source can be found in the Jupyter
Book documentation.
```