NeuroWorkshop: One Day Immersion in Neural Networks”

Dmytro Soshnikov

Senior Technical Evangelist, Microsoft

November 28

from 9:30 am till 5:00 pm

iHub

10, Khreshchatyk str

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On November 28, the workshop “NeuroWorkshop: One Day Immersion in Neural Networks” will be held under the guidance of Dmytro Soshnikov, Senior Technical Evangelist, Microsoft.

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

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.

Dmitry Soshnikov

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.

For whom:
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.

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

Block 2
4. Laboratory work. Iris Classification
5. Image analysis. Rolling net

Block 3
6. Laboratory work. Handwriting Recognition (MNIST)
7. Thin Learning Deep Networks (Batch Normalization, Dropout)
8. Analysis of sequences. recurrent networks

Block 4
9. Laboratory work. cat tags recognition
10. QA