Neural Networks and Deep Learning

Język wykładowy Angielski
Semestr Letni
Status W ofercie
Opiekun Jan Chorowski
Liczba godzin 30 (wyk.) 30 (prac.)
Rodzaj I2.Z - zastosowania inf.
Polecany dla I roku No
Egzamin Yes
Tagi PD (przetwarzanie danych) DS (Data Science)

Opis przedmiotu:

**Information for enrolled/prospective students**: all information is on SKOS (please self-enroll into Deep Learning 2021), materials are on GitHub, lectures will be streamed and recorded in Teams. **Enrollment code** is in SKOS. Neural Networks and other Deep Learning techniques allow creation of programs that are learned rather than written. This means that instead of implementing a concrete algorithm, the program applies patterns that are automatically found in the data. In example, a translation system such as the Google Translate can be created by applying a deep neural network to a large corpus of translated documents. The lecture is a continuation of the Machine Learning course, focusing on Deep Learning techniques. We will speak about the current state of the art techqniques for image recognition and language processing such as convolutional and recurrent neural networks with the attention mechanism. We will also speak about data generation techniques, such as autoencoders and generative adversarial networks and we wil see a bit of deep reinforcement learning. The lecture will be accompanied by computer exercises: * Key topics will be illustrated by small exercises. * We wil introduce the PyTorch deep learning framework. * A larger project will give you the opportunity to tackle a real world machine learning problem.