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On 9 April, the first webinar in the cycle 'Artificial Neural Networks and Deep Learning' will take place at Rīga Stradiņš University (RSU).

Working language: English

Content of the webinar

Introduction and brief overview of deep learning and artificial neural networks. The structural building block of deep learning - perceptron. Activation functions. Building neaural networks with perceptrons. Single layer neaural network. Deep (multi-layer) neural network. Training neural networks, loss optimization, gradient descent, backpropagation. Demonstration of multi-layer neural network implementation with Python and Tensorflow.

In practical part you will explore and study multi-layer perceptron neural network for image classification. A base deep neural network will be provided, you will have to change properties of it to achieve better classification results.

About the instructor

Uldis Doniņš is the Head of the Information Systems Unit of the IT Department at RSU. He holds a PhD ( in Computer Science and his field of study is software modeling and modeling formalisation. Uldis has expanded his knowledge and experience in the fields of machine learning and data intensive computing at the University at Buffalo (State University of New York, USA), School of Engineering and Applied Sciences. Being a part of Artificial Intelligence Machine Learning provides computer learning and decision-making based on the provided data that can be developed using supervised, unsupervised or reinforcement learning models. Data intensive computing deals with diverse data formats, storage models, application architectures, programming models and algorithms and tools for large-scale data analytics.

Head of the Unit
About the webinar cycle

As the power and capabilities of computing increases, Artificial Intelligence solutions takes a greater role to perform and execute various processes. Seminar is intended to provide insight into artificial neural networks, give practical examples of deep learning applications and solution implementation using Python and Tensorflow.

Participants will get hands-on experience in implementing deep learning solutions by using Python which currently is one of the most popular programming languages.

Practical part is based on individual work on implementing deep learning project.

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Upcoming webinars in this series

7 MaySeveral types of artificial neural networks, examples of their application for solving different kinds of problems
4 JuneDeep learning project presentations by each participant and discussions


online, Zoom