Deep learning
| dc.contributor.author | Zertal, Soumia | |
| dc.date.accessioned | 2026-05-24T16:41:17Z | |
| dc.date.available | 2026-05-24T16:41:17Z | |
| dc.date.issued | 2026 | |
| dc.description.abstract | This Deep Learning course handbook represents a comprehensive and rigorous exploration of one of artificial intelligence's most transformative disciplines, specifically designed for first-year Master's students specializing in Artificial Intelligence. The curriculum provides systematic coverage of both foundational principles and cutting-edge architectures that define contemporary deep learning practice. The course adopts a carefully structured pedagogical progression, beginning with theoretical foundations including neural network mathematics, activation functions, and backpropagation mechanisms, before advancing through specialized architectures: convolutional neural networks for visual data processing, recurrent networks (RNNs, LSTMs, GRUs) for sequential and temporal analysis, and generative models (GANs and VAEs) for data synthesis and augmentation. Each chapter integrates mathematical rigor with practical implementation, featuring real-world applications spanning computer vision, natural language processing, biomedical signal analysis, and autonomous systems. Students will develop essential competencies in designing, implementing, training, and critically evaluating deep neural networks while navigating architectural tradeoffs for domain-specific applications. The course emphasizes hands-on learning through exercises, projects, and implementation using modern frameworks, fostering both technical proficiency and conceptual intuition. Prerequisites include solid foundations in linear algebra, calculus, probability theory, and programming (preferably Python), along with basic machine learning concepts. Beyond technical mastery, the course instills critical awareness of ethical dimensions; fairness, transparency, privacy, societal impact, and environmental costs; that accompany powerful AI technologies. As future practitioners shaping artificial intelligence's trajectory, students are encouraged to approach these transformative capabilities with both excellence and responsibility, cultivating habits of continuous learning, rigorous thinking, and ethical innovation in a rapidly evolving field. | |
| dc.identifier.uri | http://dspace.univ-oeb.dz:4000/handle/123456789/22683 | |
| dc.language.iso | en | |
| dc.publisher | University of Oum El Bouaghi | |
| dc.title | Deep learning | |
| dc.title.alternative | Course handbook | |
| dc.title.alternative | Pedagogical Course Material Intended for First-Year Master's Degree Students | |
| dc.title.alternative | specialty artificial intelligence | |
| dc.type | Other |