Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.
Covers the fundamental concepts of machine learning, including classification, algorithms, optimization, supervised learning, reinforcement learning, and various tasks like image recognition and text generation.
Covers photonic extreme learning machines and reservoir computing, focusing on their architectures, programming techniques, and applications in optical computing.
Introduces the role of engineers in facing risks and the evolution of disasters in engineering, emphasizing the need for a scientific approach to safety and reliability.
Covers syntactic structure, dependency parsing, and neural network transition-based parsing, highlighting the importance of dependency structure in linguistic analysis.