Deep Learning and Neural Networks

Catalog description

The Neural Networks and Deep Learning course offers an extensive exploration of the fundamental principles and practical applications of artificial neural networks and deep learning. Students will delve into the core concepts of neural network architectures, activation functions, backpropagation, and model optimization. The course covers advanced topics, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep learning frameworks such as TensorFlow and PyTorch. Students will gain hands-on experience in designing, training, and fine-tuning neural networks for various applications, such as image recognition, natural language processing, and generative modeling. This course equips students with the knowledge and skills needed to excel in the dynamic and rapidly evolving field of neural networks and deep learning, making it an essential resource for those seeking expertise in this transformative domain.