The course focuses on learning neural networks using the Tensorflow library in the Python programming language. Tensorflow is currently the most popular library for creating programs that work with neural networks. The course will be carried out in the Jupyter Notebook programming environment (Colab or Anaconda) or in the Spyder environment (Anaconda).
Attendance is recommended as a prerequisite for this course:

- Python Programming Language Basics
- Python - Object Oriented Programming
- Data Processing and Visualisation in Python

Course Outline:
1. Theoretical introduction to Artificial Intelligence and Machine Learning.
2. Install and work with the TensorFlow library in the Jupyter Notebook or Spyder (Anaconda) environment.
3. Introduction to the basic concepts of neural networks: activation functions, loss functions, topology of neural networks, initialisers, metrics, callbacks, optimisers ...
4. Tensorflow - models, activation functions, loss functions, layers, initializers, metrics, callbacks, optimizers, sequential ...
5. Preparation and normalisation of input data for neural networks
6. Examples of regression and classification models for neural networks
7. Image Classification (MNIST) using Convolutional Neural Networks (CNN)
8. Design, selection and evaluation of different models