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Explore advanced deep learning concepts through this comprehensive university-level course from the University of Waterloo's Department of Statistics, taught by Professor Ali Ghodsi. Master fundamental neural network architectures including feedforward and convolutional neural networks, then progress to cutting-edge techniques like transformers, BERT, and GPT models. Delve into essential optimization and regularization methods including backpropagation, dropout, batch normalization, and layer normalization while gaining hands-on experience with Keras. Discover recurrent neural networks, attention mechanisms, and self-attention before advancing to modern transformer architectures and large language models. Learn about deep reinforcement learning principles, reinforcement learning from human feedback (RLHF), and alignment techniques used in ChatGPT. Examine generative models including variational autoencoders (VAEs), generative adversarial networks (GANs), adversarial autoencoders (AAEs), and diffusion models (DDPMs). Explore graph neural networks for handling complex data structures and understand the theoretical foundations through PAC learnability in deep learning. Gain practical implementation skills while building a solid theoretical understanding of both classical and state-of-the-art deep learning methodologies across 20 comprehensive lectures.
