Deep Learning for Computer Vision 2. Go deeper
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.
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.
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.
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 using CNNs. Before I worked as Machine Learning Researcher and I have interesting experience applying machine learning algorithms to solve different customers’ problems.
I have attended Lviv Data Science Summer School 2016 and Lviv Machine Learning Winter School and got the best project award in Computer Vision course.
Field of interests: Deep Learning, Computer Vision and Machine Learning