NEURAL NETWORKS
AND COMPUTER VISION
SERGEY
NIKOLENKO

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Deep learning, i.e., training multi-layered neural architectures, was one of the oldest tools in machine learning but has revolutionised the industry over the last decade. In this course, we begin with the fundamentals of deep learning and then proceed to modern architectures related to basic computer vision problems: image classification, object detection, segmentation, and others.

Modern computer vision is almost entirely based on deep convolutional neural networks, so this is a natural fit that lets us explore interesting architectures while at the same time staying focused and not going into too wide a survey of the entire field of deep learning. Computer vision is also a key element in robotics: vision systems are necessary for navigation, localisation and mapping, and scene understanding, which are all key problems for creating industrial and home robots.

The course is supported by Neuromation and features practical assignments done over the Neuromation platform.

Sergey Nikolenko is a computer scientist with vast experience in machine learning and data analysis, algorithms design and analysis, theoretical computer science, and algebra. He graduated from St. Petersburg State University in 2005, majoring in algebra (Chevalley groups), and earned his Ph.D at the Steklov Mathematical Institute at St. Petersburg in 2009 in theoretical computer science (circuit complexity and theoretical cryptography). Since then, Sergey  has been interested in machine learning and probabilistic modeling, producing theoretical results and working on practical projects for the industry.

Sergey Nikolenko is currently serving as the Chief Research Officer at Neuromation, leading the Artificial Intelligence Lab at the Steklov Mathematical Institute at St. Petersburg, and teaching at the St. Petersburg State University and Higher School of Economics. Dr. Nikolenko has published more than 150 research papers, including top computer science journals and conferences and several books, including a bestselling "Deep Learning" book (in Russian).

As a result of the course, the students will:

- Learn to apply Deep Learning techniques in practice


- Understand the theory behind the Deep Learning from basics to state-of-the-art approaches


- Learn how to train various deep neural architectures


- Understand a wide variety of neural architectures suited for real-life computer vision problems


- Gain essential experience with main Deep Learning frameworks

SKILLS:

- Machine Learning

- Algorithms for Networking


- Bioinformatics


- Mathematical Modeling

ABOUT SERGEY
HARBOUR.SPACE 
WHAT YOU WILL LEARN
RESERVE MY SPOT

DATE: 20 May - 7 Jun, 2019

DURATION: 3 Weeks

LECTURES: 3 Hours per day

LANGUAGE: English

LOCATION: Barcelona, Harbour.Space Campus

COURSE TYPE: Offline

HARBOUR.SPACE UNIVERSITY

RESERVE MY SPOT

DATE: 20 May - 7 Jun, 2019

DURATION:  3 Weeks

LECTURES: 3 Hours per day

LANGUAGE: English

LOCATION: Barcelona, Harbour.Space Campus

COURSE TYPE: Offline

All rights reserved. 2017

Harbour.Space University
Tech Heart
COURSE OUTLINE

Session 1

Neural network basics 
Neural networks: history and basic idea. Relationship between biology and mathematics. The perceptron: basic construction, training, activation functions.

Practice: intro to Deep Learning frameworks

Session 4

Regularisation in neural networks
Regularisation: L1, L2, early stopping. Dropout. Data augmentation.
Practice: Applying different regularisation approaches

Session 3

Optimisation in neural networks
Gradient descent: motivation, problems. Modifications, ideas: momentum, Nesterov’s momentum, Adagrad, RMSProp, adam. Second order methods
Practice: comparing gradient descent variations

Session 2

Feedforward neural networks
Feedforward neural networks. Gradient descent basics. Computation graph and computing gradients on the computation graph (backpropagation).
Practice: a feedforward neural network on classic datasets

NEURAL 
NETWORKS
AND  COMPUTER 
VISION
BIBLIOGRAPHY

Deep learning, i.e., training multi-layered neural architectures, was one of the oldest tools in machine learning but has revolutionised the industry over the last decade. In this course, we begin with the fundamentals of deep learning and then proceed to modern architectures related to basic computer vision problems: image classification, object detection, segmentation, and others.

Modern computer vision is almost entirely based on deep convolutional neural networks, so this is a natural fit that lets us explore interesting architectures while at the same time staying focused and not going into too wide a survey of the entire field of deep learning. Computer vision is also a key element in robotics: vision systems are necessary for navigation, localisation and mapping, and scene understanding, which are all key problems for creating industrial and home robots.

The course is supported by Neuromation and features practical assignments done over the Neuromation platform.

SHOW MORE
ALEXEY
DAVYDOV

Researcher at Steklov Math Institute

ABOUT ALEXEY

Alexey Davydov is a computer scientist experienced with algorithm design and machine learning. He received his bachelor degree in physics at Moscow Institute of Physics and Technology and his master degree at St. Petersburg Academic University. His main research interests are developing of competitive scheduling algorithms and usage of synthetic data in deep learning.

He has been teaching at St. Petersburg Academic University, Computer Science Center and St. Petersburg State University since 2012. Alex Davydov currently is a researcher at Steklov Math Institute where he works on theoretical research and at Neuromation where he can apply it to practice. 

SKILLS:

- Model Theory

- Algorithms


- Machine Learning


- Graph Theory

- Discrete Mathematics