What are Auto encoders & VAE (Ep28 Machine Learning Series)
What are Autoencoders and Variational Autoencoders (VAEs)?
Autoencoders are neural networks designed to learn efficient representations of data by compressing information into a smaller latent space and then reconstructing the original input. They are widely used for dimensionality reduction, anomaly detection, denoising, and feature learning.
Variational Autoencoders (VAEs) take this idea further by learning a probabilistic latent space, allowing them not only to reconstruct data but also to generate entirely new samples. VAEs became one of the foundational architectures behind modern generative AI.
In this video, we build intuition for Autoencoders and VAEs from scratch and understand how machines learn compact representations of data.
You'll learn:
• What an Autoencoder is
• Encoder and Decoder Networks
• Latent Space Representations
• Compression and Reconstruction
• Reconstruction Loss
• Denoising Autoencoders
• What a Variational Autoencoder (VAE) is
• Latent Distributions and Sampling
• Mean and Variance Vectors
• KL Divergence
• Generating New Data with VAEs
• Real-world applications in image generation and anomaly detection
By the end of this video, you'll understand how Autoencoders and VAEs learn meaningful representations of data and why they became an important milestone in generative AI.
#MachineLearning #DeepLearning #Autoencoder #VAE #GenerativeAI