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First things first, we'll cover the fundamental concepts that underpin all **deep generative models**. This includes understanding probability distributions, which is how we represent the likelihood of different outcomes. We'll explore topics like likelihood, maximum likelihood estimation (MLE), and Bayesian inference, giving you the tools to analyze data and build your own models. We'll break down the math in a way that’s easy to grasp, so don't worry if you're not a math expert. Then, we move on to learn about different types of data distributions, such as Gaussian, Bernoulli, and multinomial. Learning these distributions is crucial to understanding how generative models create new data. We'll also cover the basics of neural networks, including the importance of activation functions, layers, and how they function. This will give you a solid foundation for building and training your own generative models. You'll gain a thorough understanding of the theory behind these models and prepare you for practical applications. Having a strong grasp of these core concepts is vital to understanding the more advanced topics we'll cover later. We will take it slow, starting with simple examples and gradually increasing the complexity. By the end of this module, you'll have a solid understanding of the fundamental principles behind generative models, making the subsequent lessons much easier to understand and apply.
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