Deep Learning for AI Part 1

This is Part 1 of a two-part graduate sequence in deep learning. It establishes the foundations of modern deep learning and the core neural architectures behind today's AI systems. You will build from how neural networks learn—through forward propagation and backpropagation—to convolutional networks for computer vision, recurrent networks for sequence data, and the first generative architectures: variational autoencoders, generative adversarial networks, and Transformers. The course emphasizes both conceptual understanding and hands-on implementation in TensorFlow/Keras and PyTorch. Part 2 continues with advanced generative modeling.