Deep Learning for Computer Vision 2. Go deeper

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: Advanced overview of topologies. Structure, accuracy, size, inference time.
  • Lecture II : Tips and tricks for finetunning neural networks. Light weight neural networks.
  • Lecture III : Light weight neural networks (part 2). Ways to reduce model’s size/speed saving accuracy level.


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.



Veronika Yurchuk
Machine Learning Researcher at Ring Labs Kyiv

I am a Deep Learning Researcher at Ring Labs Kyiv with a focus on Computer Vision. I have worked on solving object detection and object classification problems. Also, I am last year Master’s student in Statistics at Mechanical and Mathematical Faculty at Taras Shevchenko National University of Kyiv.

Field of interests: Deep Learning, Computer Vision and Machine Learning

Contacts[email protected]