This course introduces the fundamental concepts and techniques of Deep Learning (DL), covering the theoretical foundations, architectures, and practical applications of artificial neural networks. Students will explore how deep learning models are designed, trained, and optimized for solving real-world problems in computer vision, natural language processing (NLP), and generative modeling. The course combines mathematical concepts, theoretical insights, and hands-on coding in PyTorch/TensorFlow to equip students with both analytical and practical skills in deep learning.
By the end of the course, students will develop and train deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs) for various tasks, including image classification, text generation, and reinforcement learning.
By the end of the course, students will develop and train deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs) for various tasks, including image classification, text generation, and reinforcement learning.
- Teacher: Muhaza Liebenlito