Transfers ToCSUDescriptionThis course offers an introduction to deep learning theories, principles, and practices. Students will explore neural networks, including perceptrons, gradient descent, and multilayer perceptrons, as well as advanced topics like convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), variational autoencoders (VAEs), and attention mechanisms. By the end of the course, students will be proficient in implementing and training neural networks using frameworks like TensorFlow, Keras, scikit-learn, and PyTorch, and will be able to critically evaluate and improve deep learning models. 