Deep Learning and Reinforcement Learning

A Variational Autoencoder (VAE) is a generative model that learns a probabilistic latent representation of input data. Unlike traditional autoencoders, which focus solely on reconstructing the input, VAEs aim to capture the underlying structure and variability of the data. This is achieved by learning a continuous probability distribution (typically Gaussian) over the latent space. By encoding input data into this distribution rather than a fixed point, VAEs can generate new, diverse, and realistic data points by sampling from the learned distribution. This capability makes VAEs valuable for tasks such as image synthesis, data augmentation, and anomaly detection. Moreover, the continuous nature of the latent space enables meaningful interpolation between data points. A Variational Autoencoder (VAE) is a machine learning model that generates new data by learning to compress and decompress information. Unlike traditional autoencoders, VAEs don't just learn to copy data; they learn