Neural Network Fundamentals

Part 1:

Part 2:

Make sure to watch both the videos above for details. The videos have examples and visuals that the following text does not contain. 

Neural networks are at the heart of many modern artificial intelligence applications, from image recognition to natural language processing. But what exactly is a neural network, and how does it work?

In this lesson, we explore the fundamental components of neural networks and explain key concepts such as layers, hidden layers, input and output vectors, forward propagation, backward propagation, epochs, and batching.

What is a Neural Network?

A neural network is a computational model inspired by the way biological brains process information. It consists of layers of interconnected nodes, or neurons, that process input data and produce an output. Each neuron applies a mathematical function to its input and passes the result to the next layer.

Layers in a Neural Network

A neural network is typically composed of three types of layers:

1. Input Layer: This is the first layer in the network, where the raw data (input vector) is fed into the model. Each neuron in this layer represents one feature of the input data.
2. Hidden Layers: Between the input and output layers, there can be one or more hidden layers. These layers are called “hidden” because they do not directly interact with the outside world. Hidden layers allow the network to learn complex patterns by performing intermediate computations.
3. Output Layer: The final layer of the network produces the output vector, which represents the network’s prediction or classification. The number of neurons in the output layer typically corresponds to the number of classes in a classification problem or the dimensionality of the output in a regression problem.

How Does a Neural Network Work?

Forward Propagation

The process of passing input data through the network to generate an output is called forward propagation. Here’s how it works:

1. Input Vector: The input vector is passed into the input layer. Each neuron in this layer takes one element of the input vector.
2. Weighted Sum: For each neuron in the hidden layers, the input values are multiplied by corresponding weights (parameters learned during training). The weighted sums may then passed through an activation function, such as ReLU (Rectified Linear Unit) or sigmoid, which introduces non-linearity into the model.
3. Output Vector: The results from the hidden layers are propagated to the output layer, where a final set of computations produces the output vector.

Backward Propagation

Once the network generates an output, it compares the output with the actual target values (ground truth). The difference between the predicted and actual values is used to calculate the loss (error). Backward propagation is the process of updating the network’s weights to minimize this loss:

1. Gradient Descent: The network calculates the gradient of the loss function with respect to each weight using a method called gradient descent. This involves computing partial derivatives that show how much each weight contributes to the loss.
2. Weight Update: The weights are then adjusted in the opposite direction of the gradient to reduce the loss. This step is repeated for multiple iterations until the loss is minimized.

Training the Neural Network

Epochs

Training a neural network typically involves multiple epochs. An epoch is one complete pass through the entire training dataset. During each epoch, the network’s weights are updated multiple times using forward and backward propagation.

Batching

When working with large datasets, it’s often impractical to process the entire dataset at once. Instead, the dataset is divided into smaller subsets called batches. The network processes each batch separately, updating the weights after each batch. This approach, known as mini-batch gradient descent, helps improve computational efficiency and allows for better generalization.

Concluding remarks

Neural networks are powerful tools for solving complex problems in various fields, from computer vision to natural language processing. By understanding the basic components—such as layers, forward and backward propagation, epochs, and batching—you can gain insights into how these models work and why they are so effective.