DEEP LEARNING IN APPLICATIONS
RADOSLAV NEYCHEV
Neural networks are the state of the art approach in different areas as Computer Vision, Natural Language Processing, Reinforcement Learning etc. Deep neural architectures promise even better results, so it is definitely the time to get into with this area.
In this course we will start from the basics and rapidly dive into the latest results in Deep Learning. This course focuses both on practical skills and theoretical background to provide the students both deep understanding and ability to work on their own in Deep Learning area.
The course main framework is PyTorch/
Ph.D. student at Moscow Institute of Physics and Technology,
Senior Quantitative Analysis Officer at Raiffeisen Bank Russia
Radoslav Neychev is a data scientist with focus on Deep Learning and Reinforcement Learning techniques. He has worked on a variety of research (CERN LHCb, MIPT Machine Intelligence Lab, CC RAS) and industrial projects (Yandex, RaiffeisenBank) in different domains vary from particle identification problem to fraudulent transactions detection.
Radoslav graduated from Moscow Institute of Physics and Technology, majoring in Applied Mathematics and Machine Learning. Radoslav is reading lectures and organising practical classes at Russian top-tier universities, tech companies and summer schools.
As a result of the course, the students will:
- Learn to apply Deep Learning techniques in practice
- Get familiar with both fundamental and most recent approaches in Natural Language Processing and Reinforcement Learning
- Get ready to face the real world problems and to apply the Deep Learning techniques to them
- Gain essential experience with PyTorch framework
SKILLS:
- Deep Learning
- Programming
- Data Processing
- System & Virtualisation
ABOUT RADOSLAV
WHAT YOU WILL LEARN
DATE: 8 Jun - 26 Jun, 2020
DURATION: 3 Weeks
LECTURES: 3 Hours per day
LANGUAGE: English
LOCATION: Barcelona, Harbour.Space Campus
COURSE TYPE: Offline
RESERVE MY SPOT
DATE: 8 Jun - 26 Jun, 2020
DURATION: 3 Weeks
LECTURES: 3 Hours per day
LANGUAGE: English
LOCATION: Barcelona, Harbour.Space Campus
COURSE TYPE: Offline
All rights reserved. 2017
COURSE OUTLINESession 1
Natural Language Processing intro
Main problems in NLP. Text classification and generation. Deep Learning techniques in NLP. Regularization in DL recap. Word Embeddings recap.
Session 4
Attention in Encoder-Decoder architecture
Encoder-Decoder architecture bottleneck. Attention mechanism. Attention outside NLP.
Session 3
Neural Machine Translation
Machine Translation and Neural Machine Translation. Encoder-Decoder architecture, sequential modeling.
Session 2
Convolutional Neural Networks in text classification.
CNN approach to context analysis. Similarities and differences from RNN.
DEEP LEARNING IN APPLICATIONS
BIBLIOGRAPHY
"Deep Learning (Adaptive Computation and Machine Learning series)" by Ian Goodfellow,Yoshua Bengio & Aaron Courville (The MIT Press, 2016)
State of the art approaches in different domains of Artificial Intelligence are based on Deep Learning techniques (e.g. in Computer Vision, Natural Language Processing, Reinforcement Learning, etc.) Deep neural architectures show great potential and promise even better results, so now is definitely the time to explore this field.
In this course we will start from the basics and rapidly dive into the latest results in Deep Learning, focusing on the NLP and RL domains. This course focuses both on practical skills and theoretical background to provide the students thorough theoretical knowledge and ability to work on their own in the Deep Learning area.
This course accompanies the Machine Learning course (Module 7).
Programming assignments will be implemented in Python 3. PyTorch framework will be used for Deep Learning practice.
"Deep Learning: Methods and Applications. Foundations and Trends in Signal Processing" by L. Deng & D. Yu (Now Publishers Inc, 2014)
ANASTASIA IANINA
Ph.D. student at Moscow Institute of Physics and Technology,
Research Scientist at Samsung AI Center,
Research Engineer at Gigster
ABOUT ANASTASIAAnastasia Ianina graduated from Moscow Institute of Physics and Technology with a major in Computer Science. She received thorough knowledge of math and machine learning, and gained a significant amount of hands-on experience: interning at Lyft and working on self-driving cars, holding a Data Scientist position at Yandex, working as a researcher at the MIPT machine intelligence lab, and writing papers to top-level international conferences.
Anastasia’s research interests include Machine Learning, Natural Language Processing, Text Analytics and Deep Learning. Mainly, she is focusing on Natural Language Understanding and topic-based text representations.
Anastasia currently teaches students from MIPT Machine Learning and Natural Language Processing. Moreover, she takes part in creating online educational courses: she authored the course “Dynamic Neural Network Programming with PyTorch” for Packt Publishing, co-authored online-course “Neural Networks and Natural Language Processing” and worked on Coursera NLP specialisation.
SKILLS:
- Python
- Deep Learning
- Reinforcement Learning
- Algorithms
- Computer Vision
- Data Structures
HARBOUR.SPACE Harbour.Space is a university created by entrepreneurs for entrepreneurs. We focus on meeting the demands of the future, while traditional education providers are too often stuck in the past.
We’re one of the only European institutions completely dedicated to technology, design and entrepreneurship, and our interdisciplinary courses are taught by some of today’s leading professionals. Our aim is not only to equip students with the knowledge to take on the real world, but to nurture, create and shape tomorrow’s tech superstars.
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