Lecture
This lecture introduces the fundamentals of deep learning, focusing on the mapping between input and output variables through neural networks. The instructor emphasizes the importance of understanding data, architecture, loss functions, and training strategies. The course aims to equip students with the ability to justify choices in model training and testing, interpret performance, and analyze limitations. Ethical considerations in data acquisition and model deployment are also highlighted, stressing the responsibility of future practitioners to make informed decisions. The lecture covers various applications of deep learning, including natural language processing, computer vision, and robotics. Students are encouraged to engage in discussions about the societal impact of deep learning and the importance of embedding values and principles in their work. The session concludes with an overview of the course structure, including lectures, practice exercises, and a group project, fostering an interactive learning environment.