## Encoder Component
The encoder component of an autoencoder is tasked with **encoding the input data into a latent representation**.      
Utilizing a basic CNN architecture, the convolutional layers capture **important image features,** while pooling layers **reduce the dimensionality** of these extracted features. Finally, the dense layer **generates latent values** that represent the image encoding.
```python
# Define the input placeholder
input_img = Input(shape=(28, 28, 1))

# Encoder
x = Conv2D(32, (2, 2), activation='relu', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(64, (2, 2), activation='relu', padding='same')(x)
encoded = MaxPooling2D((2, 2), padding='same')(x)

# Latent space
latent_dim = 16
x = Flatten()(encoded)
latent = Dense(latent_dim, activation='sigmoid', name='latent')(x)
```

## Decoder Component
The autoencoder's decoder component aims to **reconstruct the original input** from the latent representation.   
Using a simple CNN architecture, the decoder applies convolution layers to process the latent features and refine their details, followed by **upsampling layers** to expand their spatial dimensions.
```python
# Decoder
decoder_input = Input(shape=(latent_dim,))
x = Dense(7*7*64, activation='relu')(decoder_input)
x = Reshape((7, 7, 64))(x)
x = Conv2D(64, (2, 2), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(32, (2, 2), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
decoded_output = Conv2D(1, (2, 2), activation='linear', padding='same')(x)
```

As a result, we have an architecture that **compresses the image** into a lower dimension and then restores it from this compressed representation. This compression must retain the **most valuable and important features of the image**, allowing us to reconstruct the original image with the highest possible quality.

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.

Image Synthesis Through Generative Networks

Encoder - Decoder Principle

Encoder Component

Decoder Component