Deep Learning for Computer Vision 2017

Deep Learning for Computer Vision 2017

Course Description

The course aims to present basics knowledge of modern approaches which are used for solving computer vision problems: from descriptions of solutions based on deep convolution networks with hacks and practical examples.

Course topics

Theory part (lectures):

  • Lecture I: Introduction to Deep learning, Types of Deep Learning commonly used in CV tasks
  • Lecture II : Convolutional Neural Network (CNN) for image classification, Recurrent Neural Network (RNN/LSTM) + CNN for action recognition.



  • NN, CNN, RNN fundamentals.
  • Convolutional Neural Network (CNN) for image classification dataset: cifar10 [may change]; topologies: starting from feed-forward -> LeNet like -> VGG like
  • Recurrent Neural Network (RNN) + CNN for action recognition in video dataset: one from UCF family (UCF-50 or UCF-100) [may change] topologies: pre-trained CNN models (VGG-* or ResNet-*) + LSTM model + additional info

Course tools

Python, NN framework: pytorch


Basic linear algebra, proficiency in Python, machine learning basics (understanding of different types of learning (supervised, unsupervised, reinforcement learning), classification, regression problems, generalization error, overfitting, train/test datasets split). Optional, but desirable: neural network (NN) basics, feed forward NN, different activation functions, backpropagation.


Andrii Lyubonko
Senior Engineer at Samsung R&D Institute Ukraine

I came to Computer Science with natural science background. I got my Master degree in theoretical physics from Taras Shevchenko National University of Kyiv in 2004. Then I continued my research in physics in Max Planck Institute of Complex System in Dresden. Currently, I am working as a senior research engineer in Samsung Research Institute in Kyiv.

Field of interests: Deep Learning, Computer Vision

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