---
title: "Building GANs, Diffusers, and Transformer Models using Neural Networks"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# Building GANs, Diffusers, and Transformer Models using Neural Networks


## Neural Networks: The Building Blocks of AI

**Neural networks** are a type of machine learning model inspired by the structure and function of the human brain. They are composed of interconnected nodes, called neurons, that work together to process and learn from data.

### How Neural Networks Work

1. **Input Layer:** This is where the data is introduced to the network. Each neuron in the input layer represents a feature of the data.
2. **Hidden Layers:** These layers process the input data and extract relevant features. They can have multiple layers, each learning different patterns and relationships.
3. **Output Layer:** This layer produces the final output of the network, which can be a classification, prediction, or generation.

![neural network](nn.png "a neural network")

#### **Example: Handwritten Digit Recognition (MNIST Dataset)**

One of the classic examples of using neural networks in traditional ML is the MNIST dataset, where the task is to classify handwritten digits (0-9).

**Steps Involved:**

1. **Input Layer:** Each digit image (28x28 pixels) is flattened into a vector of 784 features (one for each pixel).
2. **Hidden Layer:** A single hidden layer of, say, 128 neurons, applies a non-linear activation function like ReLU (Rectified Linear Unit) to the weighted inputs.
3. **Output Layer:** The output layer has 10 neurons, one for each digit class. The softmax activation function is applied to provide a probability distribution across the classes.

**Training:** The network is trained using labeled examples of handwritten digits. The weights are adjusted through backpropagation and gradient descent to minimize the classification error.

**Output:** Given a new image of a digit, the network predicts which digit (0-9) it most likely represents.

![animation](animation.gif)

### Types of Neural Networks

* **Feedforward Neural Networks:** The simplest type, where information flows in one direction from the input to the output layer.
* **Recurrent Neural Networks (RNNs):** Designed to process sequential data, like text or time series. They have feedback connections that allow them to remember past information.
* **Convolutional Neural Networks (CNNs):** Specialized for processing grid-like data, such as images. They use filters to extract features from the data.
* **Generative Adversarial Networks (GANs):** Consists of two neural networks: a generator and a discriminator. They are used to generate new data, such as images or text.

### Example: Image Classification

Imagine training a neural network to classify images as cats or dogs.

1. **Input Layer:** The input layer would have a large number of neurons, each representing a pixel in the image.
2. **Hidden Layers:** The hidden layers would extract features from the image, such as edges, corners, and textures.
3. **Output Layer:** The output layer would have two neurons, one for "cat" and one for "dog." The neuron with the highest activation would indicate the predicted class.

### Activation Functions

Activation functions introduce non-linearity into the network, allowing it to learn complex patterns. Common activation functions include:

* **Sigmoid:** Maps values between 0 and 1
* **ReLU:** Returns the maximum of 0 and the input
* **Tanh:** Maps values between -1 and 1

**In summary, neural networks are powerful tools that can learn from data and perform complex tasks. They have revolutionized various fields, including computer vision, natural language processing, and robotics.**

## Weights in Neural Networks

**Weights** in neural networks are numerical values that determine the strength of the connections between neurons. They are essentially parameters that the network learns during the training process.

**Think of weights as the "volume" of a connection.** A higher weight means a stronger connection, while a lower weight means a weaker connection. When a neuron receives input from other neurons, the weighted sum of these inputs is calculated to determine the neuron's activation.

**Here's a simplified example:**

Imagine a simple neural network with two input neurons (A and B) and one output neuron (C). The connections between these neurons have weights:

* Weight of A to C: W_AC
* Weight of B to C: W_BC

If the inputs to A and B are X_A and X_B, respectively, then the activation of neuron C is calculated as:

```
Activation_C = W_AC * X_A + W_BC * X_B
```

During training, the neural network adjusts these weights to minimize the error between its predicted output and the correct output. This is achieved using algorithms like backpropagation.

**In essence, weights are the key to a neural network's ability to learn and generalize from data.** By adjusting the weights, the network can learn complex patterns and relationships in the data.

## Forward and Backward Propagation in Neural Networks

Neural networks learn by adjusting their weights to minimize the difference between their predicted outputs and the actual outputs. This process involves two main steps: **forward propagation** and **backward propagation**.

### Forward Propagation

* **Input:** The input data is presented to the network.
* **Calculation:** The input data is passed through the network layer by layer. At each layer, the weighted sum of the inputs is calculated, and an activation function is applied to determine the output.
* **Output:** The final output of the network is produced.

### Backward Propagation

* **Error Calculation:** The difference between the predicted output and the actual output (error) is calculated.
* **Gradient Calculation:** The gradients of the error with respect to the weights are calculated. This tells us how much each weight contributes to the error.
* **Weight Update:** The weights are updated using an optimization algorithm, such as gradient descent, to minimize the error.

**In essence, forward propagation is the process of making a prediction, and backward propagation is the process of learning from the prediction's mistakes.**

**Here's a simplified example:**

Imagine a simple neural network with one input neuron, one hidden neuron, and one output neuron.

1. **Forward propagation:** The input is multiplied by the weight of the connection between the input and hidden neuron, and the result is passed through an activation function. The output of the hidden neuron is then multiplied by the weight of the connection between the hidden and output neuron, and the result is passed through another activation function to produce the final output.
2. **Backward propagation:** The error between the predicted output and the actual output is calculated. The gradients of the error with respect to the weights are calculated. The weights are updated using an optimization algorithm to minimize the error.

This process is repeated many times with different training examples until the network learns to make accurate predictions.

## How Neural Networks Embody Intelligence: A Breakdown

**Neural networks** are a type of machine learning model inspired by the human brain. While they don't possess consciousness or sentience in the same way humans do, they can exhibit intelligent behavior by learning from data and making complex decisions.

### Key Factors Contributing to Neural Network Intelligence:

1. **Learning from Data:** Neural networks learn from large datasets. They identify patterns, relationships, and correlations within the data that humans might not be able to discern.
2. **Weight Adjustment:** The weights in a neural network determine the strength of connections between neurons. Through a process called backpropagation, the network adjusts these weights to minimize errors and improve its performance. This allows the network to learn and adapt over time.
3. **Activation Functions:** These functions introduce non-linearity into the network, enabling it to learn complex patterns that linear models cannot.
4. **Architecture and Layers:** The architecture of a neural network, including the number of layers and neurons, determines its capacity to learn and represent complex functions. Deeper networks can often learn more complex patterns.
5. **Large Datasets:** Neural networks require large amounts of data to learn effectively. The more data they are trained on, the better they can generalize to new, unseen data.
6. **Computational Power:** The ability to train large neural networks with millions or billions of parameters requires significant computational resources. Advances in hardware and software have made this possible.

### Examples of Neural Network Intelligence:

* **Image Recognition:** Neural networks can accurately identify objects, people, and scenes in images.
* **Natural Language Processing:** They can understand and generate human language, enabling tasks like machine translation and text summarization.
* **Game Playing:** Neural networks have achieved superhuman performance in games like Go and chess.
* **Recommendation Systems:** They can suggest products or content based on user preferences and behavior.

**While neural networks don't possess consciousness or subjective experiences, their ability to learn from data, adjust their behavior, and perform complex tasks makes them a powerful tool for artificial intelligence.** It's important to note that the intelligence exhibited by neural networks is a form of computational intelligence, which is distinct from human intelligence.
