At present, all the major successes of artificial intelligence are connected with neural networks and deep learning. Neural networks can recognize human emotions on photos, turn pictures into paintings in the style of famous artists, understand and synthesize natural language.
During the training, you will learn all the basic concepts and learn how to use the Microsoft Cognitive Toolkit (CNTK) toolkit for image recognition and text synthesis.
This event is intended for those who want to understand in a single day what Neural networks and deep learning are. Learn to practice tasks such as image recognition, natural language, use methods in prediction tasks and NLP.
Requirements for participants:
Python will be useful for successful participation, at least at program level. Access to the Microsoft Azure Cloud is desirable, but not mandatory.
Senior Technical Evangelist, Microsoft
Dmitry has been in Microsoft for over 10 years. As a senior technology evangelist, he is engaged in popularizing the most advanced Microsoft technologies, as well as their application in practice for digital transformation in various companies and projects. He personally conducted dozens of hackathons, frequent speaker at profile IT conferences.
Candidate of Physical and Mathematical Sciences, Associate Professor, teaches artificial intelligence and functional programming in the MFTI, NDU HSE, MAI. He is the author of several books and online courses. In his spare time, he involves children into technology, deals with digital magic and conducts Chinese tea ceremonies.
9:30 – 10:00 Registration
10:00 – 11:45 Block 1
1. Introduction to Azure Notebooks and Python
2. Introduction to the neural network. single-layer perceptron
3. Introduction to the Cognitive Toolkit. Solving the problem of classification by single-layer and multilayer networks
11:45 – 12:00 Coffee Break
12:00 – 13:30 Block 2
4. Laboratory work. Iris Classification
5. Image analysis. Rolling net
13: 30-14: 30 Break for lunch
14:30 – 16:00 Block 3
6. Laboratory work. Handwriting Recognition (MNIST)
7. Thin Learning Deep Networks (Batch Normalization, Dropout)
8. Analysis of sequences. recurrent networks
16:00 – 17:00 Block 4
9. Laboratory work. cat tags recognition
Language of presentation: Russian
Date: November 28
Venue: iHUB, 10 Khreshchatyk Street, Kyiv