On 7 May, the second webinar in the cycle 'Artificial Neural Networks and Deep Learning', themed 'Several types of artificial neural networks, examples of their application for solving different kinds of problems', will take place at Rīga Stradiņš University (RSU).
Content of the webinar
Includes Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Generative Adversarial Networks (GAN). Demonstration of CNN, RNN, and GAN implementation with Python and Tensorflow.
Practical part is planned as selection of two options for individual project - either develop an application of deep neural network using Python and Tensorflow or write a one-page review of a deep learning paper.
About the instructor
Uldis Doniņš is the Head of the Information Systems Unit of the IT Department at RSU. He holds a PhD (Dr.sc.ing.) 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.
- 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.
Next webinar in this series
| 4 June | Deep learning project presentations by each participant and discussions |
