This course offers an introduction to Deep Learning. It covers mathematical concepts, architectures, and training
techniques for neural networks. Topics include perceptrons, multilayer networks, optimization, regularization,
convolutional networks, attention mechanisms, and transfer learning, along with practical coding projects in
Python and TensorFlow/Keras.