Neural Networks Basics Series 1 — Neural Network Enlightenment: Unveiling the Mysteries of Artificial Intelligence

8 min readJan 19, 2024

In this digitally evolving era, neural networks have become a pivotal component of technological innovation. From facial recognition on smartphones to disease diagnosis in healthcare, and the autonomous systems in vehicles, the applications of neural networks are pervasive. As a crucial branch of artificial intelligence, they enable machines to learn from experience and make intelligent decisions. With the advent of enhanced computing power and the explosion of data availability, particularly the advancement of GPUs, neural networks have become the preferred approach for developing complex and intelligent systems.

Despite their widespread application, neural networks remain a domain shrouded in mystery and technical complexity for many. To demystify this, we have crafted the “Neural Networks Basics Series,” aimed at gradually unveiling the basic principles and mechanisms of neural networks. This series is tailored for beginners stepping into the world of artificial intelligence, as well as for those looking to deepen their understanding.

In this first article of the series, we begin with the most fundamental question: What are neural networks? We will introduce their historical background, explain basic terminology, showcase simple network examples, and elucidate how neural networks learn. This will lay the groundwork for understanding more complex neural network concepts.

As we progress through the series, we will delve into multilayer perceptrons, feedforward neural networks, and explore how to train and fine-tune these networks. Finally, we will examine the applications of neural networks in the real world and look forward to their future developments.

Our goal is for readers to not only understand the workings of neural networks but also to appreciate their profound impact and potential in today’s world. Let us embark on this journey of exploration and lift the veil on the mysteries of artificial intelligence.

The History and Background of Neural Networks

The concept of neural networks is not a novelty of recent years; its history dates back to the last century. Let’s begin with these early stages of development to understand how neural networks have become an integral part of modern artificial intelligence.

1. Early Development of Neural Networks

Initial Exploration (1940s-1960s): The history of neural networks starts in 1943 with the simplified neural model proposed by the psychologist McCulloch and the mathematician Pitts, marking the inception of modern neural networks. Later, in 1958, Frank Rosenblatt developed the Perceptron, one of the earliest forms of neural networks.
The First Cool-off Period (1970s): Due to hardware limitations and certain algorithmic challenges, research in neural networks experienced a downturn in the 1970s.
Revival (1980s-1990s): The field witnessed a resurgence in the late 1980s and early 1990s, thanks to more powerful computers and new algorithms, such as backpropagation.

2. The Rise of Artificial Intelligence and Machine Learning

Post-2000 Era: With the advent of big data and significantly enhanced computational power, particularly the development of GPUs, neural networks once again became a focal point of research. This era, especially after 2012, saw breakthroughs in deep learning (a form of deep neural networks) in challenging tasks like image recognition and natural language processing.
AI Boom: These achievements spurred a new wave of interest in artificial intelligence, making neural networks one of the most focused-on technologies in both research and industrial applications.

3. Comparison with Traditional Algorithms

Flexibility: While traditional algorithms often rely on explicit instructions and rules, neural networks learn decision-making rules by learning from data, offering greater flexibility.
Handling Non-linearity: Neural networks excel at handling non-linear and complex problems, which are often challenging for traditional algorithms.
Automatic Feature Extraction: Unlike traditional methods that require manual feature design, neural networks can automatically learn and extract features from data, especially crucial in complex tasks like image and speech recognition.

This history of neural networks shows that they are a technology matured over years of exploration and development. From initial theoretical models to contemporary applications in deep learning, neural networks have undergone several transformations and revivals, becoming an indispensable part of modern technology. As technological advancements continue, we can anticipate neural networks playing an even more significant role in future innovations.

Basic Terminology Explained

Understanding neural networks begins with familiarizing oneself with some basic terminologies. These terms help us describe and comprehend how neural networks are constructed and function.

1. Neurons

Principle of Biological Neurons: In biology, neurons are the fundamental working units of the brain, responsible for receiving, processing, and transmitting information. They connect with other neurons through synapses, receiving signals from other neurons.
Neurons in Neural Networks: In artificial neural networks, a neuron is a mathematical function that simulates the behavior of biological neurons. It receives inputs from the previous layer of neurons, applies weights to these inputs, sums them up, and then passes this sum through an activation function to produce an output.

2. Weights and Biases

Role of Weights: In a neural network, weights determine the importance of input signals. Each input signal is multiplied by its respective weight. These weight values are adjusted during the training process to help the network learn and make predictions more accurately.
Function of Biases: Biases are another crucial parameter in a neuron’s function, acting as an additional input independent of the actual inputs. Biases ensure that even if all inputs are zero, the neuron can still have an activation.

3. Activation Functions

Purpose: The primary role of activation functions is to introduce non-linearity into the neural network. Without an activation function, a neural network, regardless of its layers, would essentially be able to process only linear relationships.
Importance: By using activation functions, neural networks can learn and simulate complex non-linear relationships, which is essential for dealing with real-world complex tasks. Common activation functions include Sigmoid, ReLU (Rectified Linear Unit), and Tanh.

Understanding these basic terminologies is key to delving deeper into more complex concepts of neural networks. They are the building blocks that make up the structure and functionality of neural networks. In subsequent articles, we will explore how these terms apply and vary in different types of neural networks.

A Simple Network Example

To better grasp the basic principles of how neural networks work, let’s start with the simplest form of a neural network structure — the Single Layer Perceptron.

1. Single Layer Perceptron

Basic Structure: The single layer perceptron is the simplest form of a neural network, consisting of one layer of input nodes connected to a single output node. Each input node is connected to the output through a weight, and the output node has a bias term.
Functioning: Input signals (such as numbers or binary data) are fed into the perceptron, and each input is multiplied by its corresponding weight. All these weighted inputs are then summed up, added to the bias, resulting in a single numerical value. This value is then passed through an activation function to produce the final output.

2. Processing Data

Example: Consider a basic single layer perceptron tasked with deciding whether to grant a credit card to an applicant. Inputs might include the applicant’s age, annual income, and credit score.

Process:

Each input (age, income, credit score) is assigned a weight.
These inputs are multiplied by their respective weights and all added together.
A bias is added to this sum.
The total sum is passed through an activation function (e.g., a threshold function where if the sum exceeds a certain value, the output is 1, indicating credit card approval, otherwise 0, indicating rejection).

Through this simple example, we can see that even the most basic single layer perceptron can perform some basic decision-making tasks. It learns to make more accurate predictions by adjusting its weights and biases. While single layer perceptrons have significant limitations in handling complex problems, they lay the foundation for understanding more sophisticated multilayer neural networks.

In the next article, we will further explore how adding more layers and neurons enhances the network’s capabilities and how these modifications enable neural networks to process more complex, abstract problems.

How Neural Networks Learn

The core of neural network learning lies in two key concepts: Loss Functions and Backpropagation. These mechanisms work together, enabling neural networks to learn from data and optimize their performance.

1. Loss Functions

Role: A loss function measures the difference between the network’s predictions and the actual values. It is an indicator of the network’s performance, telling us how accurate its predictions are.
Types: Different types of tasks use different loss functions. For example, regression problems (like predicting house prices) might use Mean Squared Error, while classification tasks (like image classification) might use Cross-Entropy.
Function: The loss function guides the learning direction of the network. The goal of the network is to minimize the value of the loss function, reducing the discrepancy between predictions and actual values.

2. Backpropagation

Basic Concept: Backpropagation is an algorithm for training neural networks, where error information is passed backwards through the network, updating the weights and biases of the network layer by layer.

How it Helps Learning:

After the neural network completes a forward pass (processing the data to produce output), it computes the loss function.
The backpropagation algorithm then calculates the gradients of the loss function with respect to the network’s weights. These gradients indicate how much the loss function changes with each weight.
Using these gradients, the network adjusts its weights and biases through optimization algorithms like gradient descent to reduce the loss.
By iterating this process multiple times, the network gradually learns and improves its performance.

The learning process of a neural network is essentially an iterative optimization process, aiming to minimize the loss function. Through backpropagation, the network understands the accuracy of its predictions and adjusts its parameters accordingly. This learning mechanism enables neural networks to handle complex, non-linear problems, underpinning their powerful capabilities.

In the next article, “Neural Networks Basics Series 2 — Multilayer Perceptron (MLP),” we will dive deeper into multilayer networks and the foundations of deep learning. By introducing hidden layers, MLPs demonstrate how neural networks can achieve more advanced functions and decision-making through deeper learning.

Conclusion

In this article, we have explored the fundamental principles and components of neural networks. Starting from the simplest neural network structure, the single layer perceptron, to understanding how neural networks learn through loss functions and backpropagation, we have laid the groundwork for comprehending more complex neural network concepts. Neural networks, a cornerstone of artificial intelligence technology, are transforming our approach to problem-solving and decision-making in various fields.

Neural networks are significantly impacting the future of technology. They are not just revolutionizing academic research but are also reshaping everyday life aspects. From autonomous vehicles to intelligent voice assistants, neural networks are driving technological progress and opening new possibilities for the future.

Key Points Not Covered

Optimization Algorithms: While we briefly mentioned gradient descent, there are numerous other optimization algorithms, such as Stochastic Gradient Descent (SGD) and Adam optimizer, which play a crucial role in practical applications. These algorithms help the network learn more efficiently and reduce the consumption of computational resources.
Complexity and Diversity of Network Architectures: The architecture of neural networks can be extremely complex, ranging from simple single-layer networks to deep networks with millions of neurons. Different architectural designs enable neural networks to be applied across a broad spectrum of tasks and challenges.

In the upcoming article, “Neural Networks Basics Series 2 — Building Intelligence: The Mysteries of Multilayer Perceptrons and Deep Learning,” we will delve into the fundamentals of multilayer networks and deep learning. Multilayer perceptrons, with one or more hidden layers, offer the ability to process more complex data and tasks. We will explore how these networks are constructed, how they make more complex decisions and predictions through deep learning, and their practical applications.

Additionally, we will discuss the roles and choices of different activation functions and how multilayer networks are applied to solve real-world problems. This will open the door to more advanced neural network concepts, preparing for an understanding of the most sophisticated artificial intelligence technologies in today’s world.