In this video, we continue to take a deep dive into the fascinating world of Deep Learning Generative Models, exploring how machines can learn hidden structures in data and even generate new, realistic samples. 🚀

We’ll walk through two powerful approaches that form the backbone of modern AI:

🔹 Probabilistic Principal Component Analysis (PPCA) – Discover how PPCA extends traditional PCA into a probabilistic framework, enabling more flexible dimensionality reduction and data interpretation.

🔹 Variational Autoencoders (VAEs) – Learn how VAEs combine deep learning with probability theory to create a powerful generative model that can capture complex data distributions and generate new, meaningful data points.

Whether you’re a student, researcher, or AI enthusiast, this video will help you build a strong intuition for how generative models work and why they are so important in today’s AI revolution. 🌍✨

👉 If you’re excited about Deep Learning, AI, and Generative Models, don’t forget to:
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