DS412
Deep Learning in Applications
Faculty Profiles
Radoslav Neychev
Harbour.Space AI Track Director, Girafe-ai founder
Anastasia Ianina
Research Scientist at Meta Reality Labs
Course length
Duration
Total hours
Credits
Language
Course type
Fee for single course
Fee for degree students
Skills you’ll learn
Overview
State of the art approaches in different domains of Artificial Intelligence is 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 with thorough theoretical knowledge and ability to work on their own in the Deep Learning area.
This course accompanies the Machine Learning course (Module 8).
Programming assignments will be implemented in Python 3. The PyTorch framework will be used for Deep Learning practice.
Learning highlights
- 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 real-world problems and to apply the Deep Learning techniques to them
- Gain essential experience with the PyTorch framework
Course outline
15 classes
Natural Language Processing intro
Main problems in NLP. Text classification and generation. Deep Learning techniques in NLP. Regularization in DL recap. Word Embeddings recap.
Convolutional Neural Networks in text classification.
CNN approach to context analysis. Similarities and differences from RNN.
Neural Machine Translation
Machine Translation and Neural Machine Translation. Encoder-Decoder architecture, sequential modeling.
Attention in Encoder-Decoder architecture
Encoder-Decoder architecture bottleneck. Attention mechanism. Attention outside NLP.
Transformers in NLP
Self-attention technique. Transformer architecture overview.
Contextual Embeddings
Transformer-based contextual embeddings. ELMo, BERT, GPT-2, XLM overview.
Question Answering
Q&A systems. Bi-directional attention flow (BiDAF)
Midterm test
NLP open problems. Discussion, section outro.
Introduction to Reinforcement Learning
Reinforcement Learning problem statement. Stochastic and black-box optimization.
Value-based methods in RL
Discounted reward in RL. Value iteration. Policy iteration.
Model-free learning. Q-learning, SARSA
On the policy and off-policy algorithms. N-step algorithms.
Approximate Q-learning
Value function approximation using complex functions and neural networks. DQN. Experience replay.
Policy gradient methods
Policy gradient. REINFORCE algorithm. Advanced actor-critic.
RL outside games
Policy gradient as an optimization approach in different areas. Policy gradient for sequence modeling.
Final test
RL open problems, discussion. Course outro.
Prerequisites
Python programming experience, PyTorch basics. At least basic knowledge of Linear Algebra, Probability Theory, Optimisation
Methodology
The course will be organized in three-hour sessions and self-study practical assignments. Sessions will contain both theoretical and practical parts with different ratios depending on the materials.
Grading
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.
See full profileAnastasia Ianina got her PhD from Moscow Institute of Physics and Technology where she focused on Natural Language Processing and Exploratory Search problems. 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, working as a researcher at the MIPT machine intelligence lab, holding research scientist positions at Yandex, Samsung and Meta, leading teams responsible for LLM training at Tinkoff bank and WB Tech, and also writing papers to top-level international conferences.
Anastasia’s research interests include Machine Learning, Natural Language Processing, Text Analytics and Large Language Models. She currently teaches MIPT students machine learning and takes part in creating online educational courses and textbooks: she authored the course “Dynamic Neural Network Programming with PyTorch” for Packt Publishing, worked on Coursera NLP specialisation and co-authored ML textbook for Yandex School of Data Analysis.
See full profileApply for this course
Deep Learning in Applications
by Radoslav Neychev, Anastasia Ianina
Total hours
45 Hours
Dates
Apr 10 - Apr 28, 2023
Fee for single course
€1500
Fee for degree students
€750
How to secure your spot
Complete the form below to kickstart your application
Schedule your Harbour.Space interview
If successful, get ready to join us on campus
FAQ
Will I receive a certificate after completion?
Yes. Upon completion of the course, you will receive a certificate signed by the director of the program your course belonged to.
Do I need a visa?
This depends on your case. Please check with the Spanish or Thai consulate in your country of residence about visa requirements. We will do our part to provide you with the necessary documents, such as the Certificate of Enrollment.
Can I get a discount?
Yes. The easiest way to enroll in a course at a discounted price is to register for multiple courses. Registering for multiple courses will reduce the cost per individual course. Please ask the Admissions Office for more information about the other kinds of discounts we offer and what you can do to receive one.