Neural Networks and Deep Learning

The textbook "Neural Networks and Deep Learning" provides a comprehensive presentation of both classical and modern models in the field of deep learning. It places a special emphasis on the theoretical foundations and algorithms that are essential for understanding neural networks and their application in various problem areas. The reader gains detailed insights into the functioning of neural architectures, the challenges of training, and the peculiarities of regularization. In addition, the connections between traditional machine learning models and neural networks are highlighted, deepening the understanding of the underlying mathematical and algorithmic concepts. The book covers various data domains such as text, image, and graphs, thus offering a broad overview of the application possibilities of deep learning. It is aimed at both students and professionals who wish to expand their knowledge in this dynamic and rapidly evolving field. The practical examples and theoretical explanations make it possible to grasp the complexity of modern neural networks and recognize their potential in solving real-world problems.