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ELBO derivation

📚 RECOMMENDED BOOKS TO START WITH MACHINE LEARNING ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ If you're new to ML, here are the 3 best books I recommend (I've personally read all of these): 1. Hands-On Machine Learning – the go-to practical ML guide: https://amzn.to/3UcGqSS 2. Mathematics for Machine Learning – deep dive into the theoretical aspects of ML: https://amzn.to/3IZgHe7 3. Designing Machine Learning Systems - practical strategies for building scalable ML solutions: https://amzn.to/4ojEqFX These are affiliate links, so buying through them helps support the channel at no extra cost to you — thanks 🙏 Summary ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ In this video, we explain the derivation of the Evidence Lower Bound (ELBO) using KL divergence and Bayes’ rule—core concepts in variational inference and probabilistic machine learning. Step-by-step, we simplify the math and reveal how ELBO helps approximate complex posterior distributions. Follow Me ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 🐦 Twitter: @datamlistic   / datamlistic   📸 Instagram: @datamlistic   / datamlistic   📱 TikTok: @datamlistic   / datamlistic   👔 Linkedin:   / datamlistic   Channel Support ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ The best way to support the channel is to share the content. ;) If you'd like to also support the channel financially, donating the price of a coffee is always warmly welcomed! (completely optional and voluntary) ► Patreon:   / datamlistic   ► Bitcoin (BTC): 3C6Pkzyb5CjAUYrJxmpCaaNPVRgRVxxyTq ► Ethereum (ETH): 0x9Ac4eB94386C3e02b96599C05B7a8C71773c9281 ► Cardano (ADA): addr1v95rfxlslfzkvd8sr3exkh7st4qmgj4ywf5zcaxgqgdyunsj5juw5 ► Tether (USDT): 0xeC261d9b2EE4B6997a6a424067af165BAA4afE1a