## Probabilistic Deep Learning Fundamentals: Introduction to Bayesian Inference

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✔ What is Bayesian inference and why is it important?

✔ How to apply Bayes' theorem to update your beliefs

✔ The components of Bayes' theorem: prior, likelihood, and posterior

✔ The Bayes' rule formula and how to use it

✔ Examples of Bayesian inference in deep learning

✔ Bayesian neural networks and how they differ from standard neural networks

✔ Variational inference and how it approximates the posterior distribution

✔ Challenges and limitations of Bayesian inference in deep learning

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Read MoreProbabilistic Deep Learning Fundamentals: Introduction to Bayesian Inference**Part 1/8**✔ What is Bayesian inference and why is it important?

✔ How to apply Bayes' theorem to update your beliefs

✔ The components of Bayes' theorem: prior, likelihood, and posterior

✔ The Bayes' rule formula and how to use it

✔ Examples of Bayesian inference in deep learning

✔ Bayesian neural networks and how they differ from standard neural networks

✔ Variational inference and how it approximates the posterior distribution

✔ Challenges and limitations of Bayesian inference in deep learning

...