Neural Networks

Neural Networks#

Mahmood Amintoosi, Spring 2024

Computer Science Dept, Ferdowsi University of Mashhad

Note

These lectures were built using the new Sphinx-based Jupyter Book 2.0 tool set, as part of the ExecutableBookProject. 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.

About Neural Networks#

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, which offers a deep dive into the language’s essentials.

  • Mathematics: Basic understanding of calculus and statistics at the undergraduate level.

  • Current students of MDS at FUM University are already familiar with the concepts covered in the FDS and MFDS courses.

Questions?#

I will be having office hours for this course on Monday (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.

Our Slack workspace#

Come and join our Slack 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. I have forked his repository and modified it to suit my own needs andpreferences. I would like to thank him for his great work and generosity.

Contents