Introduction

From face recognition to self-driving cars, and even ChatGPT, neural networks are powering the technologies that are reshaping our world. These intricate systems, inspired by the human brain, form the backbone of many artificial intelligence applications we interact with daily.

In this first part of our two-part series on neural networks, we'll explore their structure and understand how they work. Whether you're a curious beginner or a tech enthusiast looking to deepen your understanding, this guide will provide you with a solid foundation in neural networks.

By the end of this post, you'll understand:

• The basic structure of neural networks
• How information flows through these networks to produce results
• How neural networks learn and adapt to solve complex problems

In our next post, we'll dive into the fascinating mathematics that make neural networks tick. But for now, let's start by unraveling the architecture of these powerful systems.

Structure of Neural Networks

Network Architecture Neural networks are a system of connected neurons that process and transmit information to solve complex problems. Just as the human brain has billions of neurons firing and triggering one another to help us think, neural networks have their own system of neurons working together.

These neurons are organized into layers, forming the network's architecture. This structure gives the network more flexibility: layers can be arranged in various ways depending on the neural network's task. For instance, a neural network trained to recognize faces will have a different architecture from one designed to generate images. This adaptability is what makes neural networks so versatile and powerful across a wide range of applications.

Neurons Neurons, also called nodes, are the fundamental building blocks of a neural network. You can think of each neuron as a "cell" that holds a number, typically between 0 and 1. This number is called the activation value, and it indicates how strongly the neuron is firing in response to input.

Let's use an image recognition network as an example. If we input an image of an apple, the neuron associated with "apple" should have a high activation value (close to 1), indicating a strong "match". Conversely, a neuron associated with "banana" should have a low activation value (close to 0).

![[Pasted image 20241004095141.png]] (Photo from GeeksforGeeks)

Layers Within a neural network, neurons are organized into layers. Picture these layers as columns of neurons stacked on top of each other. There are three main types of layers:

1. Input Layer: This is where the network receives its initial data. In our image recognition example, this layer would process the raw pixel values of the apple image.

2. Hidden Layers: These are the intermediate layers between the input and output. They're called "hidden" because they're not directly exposed to the input or output. This is where most of the complex processing occurs, like identifying edges, shapes, and more complex features of the image.

3. Output Layer: This final layer produces the network's answer or prediction. In our example, it might have neurons representing different types of fruit, with the "apple" neuron having the highest activation.

As data flows through the network, neurons in each layer activate to varying degrees based on the input they receive. This cascade of activations continues from the input layer through the hidden layers until it reaches the output layer. The neuron with the highest activation in the output layer represents the network's final prediction.

Connections Now that we understand neurons and layers, let's explore how they're connected. Imagine lines connecting every neuron in one layer to every neuron in the next layer. These connections are called weights, and they play a crucial role in how information flows through the network.

Weights determine the strength and importance of connections between neurons. A higher weight means a stronger connection, so the activation of one neuron will have a bigger impact on the neuron it's connected to in the next layer. Conversely, a lower weight indicates a weaker connection and less influence.

While weights determine the strength of connections between neurons, each neuron also has another important parameter: the bias. The bias can be thought of as the neuron's threshold for activation or its baseline tendency to fire regardless of inputs. Here's why bias is crucial:

1. Shifting the activation function: Bias allows the neuron to shift its activation function left or right along the input axis. This shift enables the neuron to activate (or not activate) at the appropriate input levels for the task at hand.
2. Enhancing flexibility: By allowing greater modifications to the activation function, bias provides an additional degree of freedom for the neuron to adjust its response. This flexibility is key during the learning process, allowing for more precise and nuanced adjustments.
3. Enabling complex representations: With bias, neurons can learn and represent more complex decision boundaries. This allows the network as a whole to model more sophisticated relationships in the data.
4. Providing a baseline: Bias allows a neuron to have a non-zero output even when all inputs are zero. This can be crucial for neurons that need to be active by default or for representing features that are always present in the data.

In essence, bias acts like a fine-tuning knob for each neuron, giving the neural network the ability to fit a wider range of functions and learn more complex patterns from the data. Together with weights, biases form the learnable parameters of the network, continuously adjusted during training to minimize prediction errors.

Activation Function We mentioned activation functions earlier, but they're important enough to warrant further explanation. These mathematical formulas determine whether and how strongly a neuron fires based on its inputs.

Activation functions introduce non-linearity into the network, allowing it to learn complex patterns. Without them, the neural network would just be a series of linear transformations, severely limiting its problem-solving abilities.

There are various types of activation functions, each with its own characteristics. Some common ones include the sigmoid function (which outputs values between 0 and 1), the ReLU (Rectified Linear Unit) function, and the tanh function.

In conclusion, the structure of a neural network - from its overall architecture down to individual neurons, layers, connections, and activation functions - works in concert to process information and make predictions. By adjusting the number of layers, the connections between neurons, and the types of activation functions used, we can create neural networks capable of tackling a wide array of complex problems.

How Neural Networks Work

Now that we understand the structure of neural networks, let's dive into how they actually work. At its core, a neural network is all about information flow and pattern learning.

Information Flow For information to go through a neural network, it undergoes a multi-step process.

1. Data enters the neural network through the input layer. Each neuron within the layer represents a feature of the data inputted. For example, in an image recognition network, one neuron could focus on the top-left pixel while another could look at the bottom-right.
2. From there, data flows to the hidden layers. The weighted connections we discussed earlier play a crucial role here, influencing the strength of the signal as it passes from one neuron to the next.
3. At each neuron, an activation function determines the neuron's output (activation number). This is where the network introduces non-linearity, allowing it to learn complex patterns.
4. Finally, the processed information emerges from the output layer. The neuron with the highest activation will be considered the network's output. This final output is the neural network's prediction, classification, or whatever output the network was designed to produce.

Pattern Learning But how does a neural network learn to recognize patterns? It's an iterative process of trial and error:

1. The neural network starts with random weights and biases.
2. During training, the network processes many examples or inputs.
3. For each input, the network's output (guess) is compared to the expected output (answer). The difference (error) between these is quantified using a loss function.
4. Based on this error, the weights and biases are adjusted to minimize the error. This process, known as backpropagation, uses techniques like stochastic gradient descent. Basically, we try to find the most efficient way to reduce our error by nudging the weights and biases to get the answer we want.
5. Through many iterations of this process, the weights and biases are tuned to accurately predict the output for a given input.

Adaptability and Generalization Neural networks are incredibly adaptable. The combination of layers, weights, and non-linear activation functions allows them to approximate incredibly complex functions. This means they can adapt to different types of data and problems.

Moreover, neural networks can generalize - they can apply learned patterns to new, unseen data. This is crucial for real-world applications. For instance, a text recognition (OCR) neural network is able to recognize your text, even though it wasn't explicitly trained on your handwriting.

Bringing It All Together Let's use an example to summarize how neural networks work. Imagine a neural network designed to recognize handwritten digits. When you input an image of a handwritten '7':

1. The pixel values of the "7" enter the network through the input layer.
2. As the image goes through the hidden layers, neurons activate based on patterns they've learned to recognize. For example, one neuron may fire seeing a horizontal line while another may activate seeing a slanted line.
3. In the output layer, the neuron corresponding with "7" activates with the highest number.
4. In the case the network didn't classify the image as "7", it will adjust its weights and biases to do better next time.

This process repeats for thousands or millions of examples until the network becomes proficient at recognizing digits.

Conclusion

Neural networks are the driving force behind many of the AI technologies we interact with daily. By mimicking the structure and function of the human brain, they can learn to recognize patterns, make decisions, and solve complex problems.

We've explored how neural networks are structured, with layers of interconnected neurons processing information. We've also seen how they learn through a process of trial and error, adjusting their internal parameters to improve their performance over time.

This adaptability and capacity for learning make neural networks incredibly powerful and versatile. From recognizing handwritten digits to powering sophisticated language models, neural networks are at the forefront of AI innovation.

Whether you're looking to understand the technology shaping our world or considering a career in AI and machine learning, a solid grasp of neural networks is invaluable.