Deep Learning

Resource | v2 | updated by jjones |
Type Course
Created 2020
Identifier NPFL114


In recent years, deep neural networks have been used to solve complex machine-learning problems. They have achieved significant state-of-the-art results in many areas. The goal of the course is to introduce deep neural networks, from the basics to the latest advances. The course will focus both on theory as well as on practical aspects (students will implement and train several deep neural networks capable of achieving state-of-the-art results, for example in named entity recognition, dependency parsing, machine translation, image labeling or in playing video games). No previous knowledge of artificial neural networks is required, but basic understanding of machine learning is advisable.


about Deep learning

Deep learning (also known as deep structured learning) is part of a broader family of machine learnin...

precedes Deep Reinforcement Learning | ÚFAL

In recent years, reinforcement learning has been combined with deep neural networks, giving rise to g...

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9.5 /10
useless alright awesome
from 2 reviews
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10 rating 5 level 10 clarity 4 user's background

The best deep learning course and also the very best course of my master's studies overall. Up to date with latest research.
Gives very good introduction to deep neural networks and various possible applications of them in image, audio and text processing.
1 0

9 rating 5 level 7 clarity 6 user's background

Awesome practical examples for each lecture.
There is so much to cover that sometimes the topics are superficially explained and not very clear.
The main plus of this course are the challenges you can solve (all available in GitHub repo). It moves the course to whole new level if you leave theory and start coding. Be aware that the challenges get very hard towards the end. In the first ones it's basically copy / paste.