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This lecture by the instructor on Normalizing Flows covers the goal of normalizing flows in data inference and generation, the formulation of joint distributions, the taxonomy of normalizing flows, and practical considerations. It delves into elementwise bijections, linear flows, coupling and autoregressive flows, residual flows, and practical applications of normalizing flows in analyzing inverse problems. The lecture also discusses the limitations of normalizing flows and introduces the concept of generative modeling with Glow, focusing on invertible 1x1 convolutions.