DEEP LEARNING IN APPLICATIONS
RADOSLAV NEYCHEV
We offer innovative university degrees taught in English by industry leaders from around the world, aimed at giving our students meaningful and creatively satisfying top-level professional futures. We think the future is bright if you make it so.
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,
Machine Learning Instructor at BigData Team
Radoslav Neychev is a data scientist with focus on Deep Learning and Reinforcement Learning techniques. He has worked on 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 in techniques in practice
- Face Deep Learning approaches in Natural Language Processing and Reinforcement Learning
- Learn the latest approaches to various tasks in DL
- Gain essential experience with main PyTorch framework
SKILLS:
- Deep Learning
- Programming
- Data Processing
- System & Virtualisation
ABOUT RADOSLAV
HARBOUR.SPACE WHAT YOU WILL LEARN
DATE: 8 Jul - 26 Jul, 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: 8 Jul - 26 Jul, 2019
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
Word embeddings
Word representations in Machine Learning. Classical approach. Embeddings. Word2vec.
Session 4
Convolutional neural networks in text classification.
CNN approach to context analysis. Similarities and differences from RNN.
Session 3
Recurrent neural networks, seq2seq
Sequential modeling. Encoder-decoder architecture.
Session 2
Text classification tasks
Text classification using in Machine Learning and Deep Learning.
DEEP LEARNING IN APPLICATIONS
BIBLIOGRAPHY
"Deep Learning (Adaptive Computation and Machine Learning series)" by Ian Goodfellow,Yoshua Bengio & Aaron Courville (The MIT Press, 2016)
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/
"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,
Machine Learning Instructor at BigData Team
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 significant amounts 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, Deep Learning, etc. Mainly, she is focusing on Natural Language Understanding and topic-based text representations.
Anastasia currently teaches students from MIPT machine learning and works as a Machine Learning Instructor at bigdatateam.org. Moreover, she takes part in creating online educational courses: she authored the course “Dynamic Neural Network Programming with PyTorch” for Packt Publishing and worked on Coursera NLP specialisation.
SKILLS:
- Python
- Deep Learning
- Reinforcement Learning
- Algorithms
- Computer Vision
- Data Structures