Deep Learning

This course provides a comprehensive introduction to Deep Learning, covering both theoretical foundations and practical implementation. Students will explore artificial neural networks, optimization methods, and regularization techniques, before progressing to advanced architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTMs/GRUs, and attention-based models. Through hands-on sessions using TensorFlow and Keras, the course emphasizes real-world applications in computer vision and natural language processing.