Finally, we can bring it all together and create an autoencoder that restores the input image with the highest possible quality.

We will use the MNIST dataset because it is relatively simple, and the training time for our network will not be too long.

>Note
>
>You can find the source code via the following <a 
href="https://colab.research.google.com/drive/14NxgMU6EOiGzDv5lZuivUj6lPbm2kICC?usp=sharing"
target="_blank"
style="color: #ff8a00; font-weight: 600; text-decoration: none; transition: 0.3s ease-in-out;"
onmouseover="this.style.color='#ff0000'"
onmouseout="this.style.color='#d27204'">Link</a>.  If you want to run the code or even change some components, you can copy the notebook and work with the copy.


We can see that our model accurately **restores handwritten digits**.     
However, if we attempt to generate new data using samples from a Gaussian distribution, the images appear smoothed and resemble random, unstructured noise.

To address this issue, we need to **regularize our latent space** by using a Variational Autoencoder (VAE).

Discover the artistry behind image generation using generative networks in our course. From theory to practice, delve into the world of generative networks, unlocking the secrets to crafting captivating images from data.

In this section, we'll start by understanding different types of generative models. We'll look into autoencoders, which transform data, GANs, which use a competitive approach, and diffusion networks, which spread out data. You'll learn how each model generates data differently, paving the way for further exploration in generative networks.

We will implement a simple Variational Autoencoder (VAE) using the MNIST dataset. We will build and train the VAE model, which includes designing the encoder and decoder networks, sampling from the latent space using the reparameterization trick, and optimizing the combined reconstruction and Kullback-Leibler divergence loss.

Now we will explore the generator-discriminator principle fundamental to GANs, implement a simple GAN model, and examine various GAN variations. Additionally, we will provide an overview of some existing state-of-the-art image generation models.

Autoencoder Implementation