UC BerkeleyAdvanced
Designing, Visualizing, and Understanding Deep Neural Networks (CS 182)
UC Berkeley's rigorous computer science curriculum covering deep architectures, transformers, and optimization theory.
Duration45+ hours
PriceFree
FormatMixed
LevelAdvanced
What you'll learn
- Analyze mathematical foundations of backpropagation and loss landscapes
- Implement transformer attention mechanisms from fundamental matrix equations
- Understand latent diffusion, VAEs, and score-based generative modeling
- Formulate policy gradients and Q-learning for continuous control
About this course
Taught by Sergey Levine and Berkeley faculty. A complete academic deep dive into modern deep learning, spanning convnets, self-attention, generative models, diffusion, and reinforcement learning. Full syllabus, lecture slides, and homework notebooks are publicly accessible.
Prerequisites
Multivariable calculus, linear algebra, and proficiency in Python/NumPy