NYU Deep Learning
Dive into the world of deep learning with this comprehensive NYU course. Explore the history and foundations of neural networks, including gradient descent and backpropagation. Master essential concepts like convolutional and recurrent neural networks, and gain hands-on experience with PyTorch implementations. Delve into advanced topics such as energy-based models, self-supervised learning, and variational inference. Discover the applications of deep learning in computer vision, speech recognition, and natural language processing. Learn about graph convolutional networks, transformers, and attention mechanisms. Tackle optimization techniques for deep learning and explore planning and control under uncertainty. Conclude with insights into Lagrangian backpropagation and participate in a Q&A session. This course offers a thorough understanding of deep learning principles and their practical applications in various domains.