In our daily lives, we rely on many complex systems in our brains we take for granted. Think, for example, about the process of recognizing a friend’s face in a crowd. We don’t consciously think about how our brain processes the visual information and matches it to a memory. But this seemingly simple task requires a complex network of neurons firing in our brain. To create more life-like artificial intelligence, scientists and engineers have sought to mimic this capability with a machine learning technique known as a neural network.

Neural Networks 101

So, what exactly is a neural network? Simply put, a neural network is a set of algorithms that mimics the functioning of the human brain to recognize patterns in data. It’s a powerful tool that has revolutionized the field of machine learning and artificial intelligence.

Neural networks are incredibly versatile and can be used for a wide range of tasks, from image and speech recognition to natural language processing and even game playing. For example, Google’s AlphaGo, the AI program that famously defeated the world champion at the ancient Chinese game of Go, was based on a neural network. Conversational AI models like ChatGPT are another example, relying on neural networks for natural language processing.

A good way to understand how a neural network works is to break down its components. At its core, a neural network is made up of layers of interconnected nodes, known as neurons. Each neuron takes input from its neighboring neurons, processes the information using a mathematical function, and then passes the output to the next layer of neurons. This process continues until the final layer, which produces the output.

Training a Neural Network

Training a neural network involves feeding it an input dataset and adjusting its parameters to optimize its performance on a specific task. The network uses the input data to make a prediction, which is compared to the desired output. The difference between the predicted output and the desired output is used to adjust the weights of the neurons in the network, allowing it to make more accurate predictions over time.

Here are the general steps to train a neural network:

Define the problem and gather data: The first step is to define the problem that you want to solve and gather the data that you will use to train the neural network. This data should be representative of the problem and include both input and output data.

Preprocess the data: Before feeding the data into the neural network, it needs to be preprocessed. This includes tasks such as scaling the data, converting categorical data into numerical data, and splitting the data into training and testing sets.

Design the neural network architecture: The next step is to design the architecture of the neural network. This includes deciding on the number of layers, the number of neurons in each layer, and the activation functions to be used.

Initialize the weights: The weights of the neural network need to be initialized to random values before training begins.

Feed the data into the network: The training data is then fed into the neural network, and the network’s output is compared to the true output.

Adjust the weights: The weights of the neural network are then adjusted using an optimization algorithm to minimize the difference between the network’s output and the true output. Stochastic gradient descent is an example of one such algorithm.

Repeat until convergence: Steps 5 and 6 are repeated multiple times until the neural network’s performance on the testing set reaches a satisfactory level.

Evaluate the network: Once the neural network has been trained, its engineers will test its performance on unseen data.

Technologies That Use Neural Networks

Given their ability to mimic some functions of the human brain, it shouldn’t come as a surprise that companies in nearly every industry are using neural networks to build a wide range of technologies. 

Image and video recognition

Neural networks are used in image and video recognition to identify objects, people, and patterns. Because neural networks can be trained to recognize specific objects or patterns, they’re highly accurate at identifying and classifying objects in images and videos, even in complex and changing environments.

Image and video recognition are used in a wide range of applications, including security and surveillance systems, self-driving cars, and medical imaging.

Natural language processing

Neural networks are used in natural language processing to analyze and understand human language. Neural networks are fundamental to natural language processing, enabling AI models to generate natural-sounding responses and analyze the sentiment of written or spoken language.

Natural language processing is used in chatbots, sentiment analysis tools, and virtual assistants like Siri and Alexa.

Speech recognition

Neural networks are used in speech recognition to convert spoken words into text. With the ability to recognize language, accents, and speech patterns, neural networks make speech recognition technologies highly accurate at transcribing spoken language.

Speech recognition is used in speech-to-text applications, virtual assistants, and speech recognition software for individuals with disabilities.

Recommendation systems

Neural networks are used in recommendation systems to suggest products, services, and content to users based on their preferences and past behavior. By analyzing large amounts of data on user behavior and preferences, recommendation systems can create more personalized user experiences which increase engagement and revenue.

Companies are already using recommendation systems in e-commerce websites, streaming services, and social media platforms.

Financial analysis

Neural networks are used in financial analysis to analyze market trends, identify trading opportunities, and make investment decisions. Neural networks that are trained on large volumes of financial data help financial analysts and trades reduce risk and make more informed decisions.

Robotics

Neural networks are used in robotics to enable robots to learn from their experiences and make decisions based on their environment. Neural networks give robots the ability to identify objects, navigate environments, and learn from feedback. 

Key Takeaways

A neural network is a powerful tool that mimics the functioning of the human brain to recognize patterns in data. While it might seem complex, its underlying principles are surprisingly simple. As machine learning engineers continue to develop and refine neural networks, we can expect to see them become even more integral to our daily lives.