What is a neural network?

A neural network, also known as an artificial neural network, is a type of machine learning that works similarly to how the human brain processes information. Instead of being programmed step by step to follow fixed rules, it learns by directly observing patterns in data. This makes it flexible, adaptive, and surprisingly good at solving problems that don’t follow a script.

Neural networks are foundational to deep learning models, which developers rely on to build and deploy intelligent systems that feature capabilities such as image recognition, speech-to-text, natural language processing (NLP), code generation, and predictive analytics.

Neural networks show up in tools and services you might use every day, including:

• Face recognition technology in photo apps.

• Voice assistants that understand and respond to speech.

• Code generation tools that valid code, debug, and automate other tasks.

• AI-assisted medical imaging that flags potential issues.

• Autonomous vehicles that read road signs and sensor data.

How neural networks work

A neural network processes information in layers, with each layer composed of interconnected nodes called neurons. Deep learning models stack many layers together to handle more complex tasks.

Think of the layers as stages in a pipeline, where each one transforms the data before passing it on to the next layer:

• Input layers take in raw data, such as text, numbers, or image pixels.

• Hidden layers transform that data, passing it through one or more stages.

• Output layers return the result, such as a prediction, label, or recommendation.

Here’s a closer look at how a neural network works, step by step:

1. Feedforward: Making initial predictions

Everything starts with what is referred to as the forward pass. Input data moves through the network one layer at a time—from input to output—without looping back. Each neuron is randomly assigned a weight, or numerical value that determines how much influence it has on the next neuron, and each weight is combined with a small adjustment called a bias.

The neuron then passes through an activation function, which helps the network make more flexible decisions. This function is especially important if the network must deal with complex, nonlinear relationships.

Next, the network computes an output based on the current weights and biases. That output might be a class label, a score, or a probability.

Example: Let’s say a neural network model recognizes handwritten digits. Each pixel of the image is passed as an input. The model evaluates the pixel intensities, processes them through multiple hidden layers, and predicts which digit (0–9) it sees.

2. Backpropagation: Learning from mistakes

Once the network makes a prediction, it checks how close it got to the right answer using a loss function, which measures the difference between the prediction and the result.

The network then works backward using backpropagation to update the weights and biases. This process uses advanced math to figure out which parts of the network contributed to the error, gradually improving accuracy with each training cycle.

Optimization techniques—such as stochastic gradient descent, which updates the network in small, incremental steps based on feedback—help fine-tune performance over time and are common in many AI models.

Example: If a fraud detection model flags a legitimate transaction by mistake, backpropagation helps correct the error, so the model performs better next time.

3. Iteration and convergence: Improving performance

Because of the iterative nature of stochastic gradient descent methods, neural networks must be trained over many epochs—or complete passes through the training data. With each cycle, a network improves its predictions. When performance stops significantly improving, the network is said to have converged or settled into a stable state.

Example: A retail recommendation model trained on years of customer purchase history can eventually suggest relevant products, even to first-time visitors.

Benefits of neural networks

Once you understand how artificial neural networks work, it’s easier to see why developers frequently use them. These networks offer you several advantages, including the ability to:

Automate code generation and review

Neural networks are essential to AI code generation, which saves you time and effort by providing context-based suggestions as you write code. Neural networks can also automate complex tasks such as code analysis, identification of bugs and other potential issues, and testing.

Example: GitHub Copilot uses generative AI models that evaluate text in code editors. They then suggest code snippets and entire functions, enhancing developer productivity and code quality.

Quickly adapt to new scenarios

Neural networks learn from examples and can be fine-tuned for new tasks without starting from scratch. This makes them especially useful for creating and deploying machine learning models needed for business, such as for automating processes, improving predictions, and personalizing customer experiences.

Example: A retail team might train a model to predict customer churn. Later, with minimal adjustment, the same model structure could be repurposed to forecast inventory or recommend products.

Understand complex relationships

Neural networks can uncover sophisticated patterns in data, such as in natural language, that simpler models often miss. They’re especially helpful when output depends on many factors, such as behavior, timing, and context.

Example: In finance, neural networks can spot subtle signals in stock market behavior by analyzing data such as news sentiment, economic indicators, and price history, all at once.

Handle large amounts of data

With the right hardware, neural networks can accurately process huge volumes of data quickly, including text, images, audio, and time-series data. This makes them ideal for industries where speed and precision matter.

Example: In logistics, companies use neural networks to optimize delivery routes by analyzing traffic, weather, and package loads in real time.

Work with messy or incomplete input

Real-world data is rarely perfect. Neural networks can still make reliable predictions when some information is noisy, missing, or inconsistent. Also, once trained, they can generalize—meaning they make useful predictions even on data they haven’t seen before.

Example: In healthcare, an imaging model can detect signs of disease even when working with low-resolution or imperfect scans.

Learn relevant features automatically

Neural networks determine which parts of the input are most relevant for the task at hand. Unlike traditional machine learning models, they don’t need manual setup or predefined rules.

Example: In language processing, models learn grammar, context, and meaning directly from raw text, without being told how language works.

Enhance decision-making

Because neural networks can understand complex, nonlinear relationships in large data sets, they can provide more targeted insights and accurate predictions needed to guide business decisions.

Example: A manufacturer can more effectively optimize resources by using output from neural network models to accurately make forecasts and adjust inventory.

Minimize security risks

Neural networks can automate security checks and enforce policies throughout CI/CD (continuous integration and continuous deployment) workflows, helping ensure adherence to all security and compliance requirements.

Example: A developer teams relies on neural network models to proactively protect its code, infrastructure, and applications from cyberthreats and security vulnerabilities.

Types of neural networks

Neural networks have different architectures, each designed for specific types of data and tasks. Artificial neural networks can be simple or highly complex, depending on the problem they're designed to solve. Understanding the major types of neural networks and how they’re used can help you choose the right one for your project or application.

Convolutional neural networks (CNNs)

Convolutional neural networks (CNNs) are built to process visual data, such as needed for image and video recognition, visual search, and medical imaging. They scan input images in small sections using filters, picking up on patterns such as edges, colors, and textures. As data flows through the network, layers combine those local features into more complex ones, eventually identifying objects such as faces, animals, or signs.

CNNs are typically made up of:

• Convolutional layers for detecting features.

• Pooling layers for reducing detail and speeding up processing.

• Fully connected layers for generating a final prediction.

CNNs power facial recognition in photo apps and help radiologists spot disease in X-rays and MRIs.

Recurrent neural networks (RNNs)

Recurrent neural networks (RNNs) are built to handle sequential data—such as language, audio, or time-series signals—by including feedback loops that let past information influence current decisions. Some variants are specifically designed to retain context over longer sequences, improving performance on tasks such as translation, forecasting, or transcription.

RNNs are used in virtual assistants, stock forecasting tools, and language translation apps.

Feedforward neural networks (FNNs)

Feedforward neural networks (FNNs), also known as multilayer perceptrons, are the simplest type of neural network and best used for basic classification, regression, and pattern recognition. Data flows one way—from input to output—with no looping. They're great for straightforward prediction tasks or as building blocks in larger systems.

FNNs are used to predict customer churn based on demographic and purchase data.

Generative adversarial networks (GANs)

Generative adversarial networks (GANs) are made up of two competing networks: one that generates data, and one that tries to spot fakes. This back-and-forth helps the generator improve until the output becomes highly realistic.

GANs are used to enhance low-resolution images, generate synthetic photos, or simulate product designs in fashion and gaming, making them a key part of the generative AI model landscape.

Transformers use a technique called self-attention to understand the context of words in a sentence, regardless of their position. They can process data in parallel, which makes them faster and more efficient than older models such as RNNs.

Transformers underlie today’s most advanced language tools, including chatbots, code assistants, and generative AI models used to create, translate, and summarize content at scale. They’re also central to many breakthroughs in AI in software development, enabling faster iteration and smarter automation throughout the development lifecycle.

Neural networks and deep learning: How they’re connected

The terms neural networks and deep learning are often used interchangeably, but they’re not quite the same. Understanding how they relate and what sets them apart can help you better grasp how many modern AI tools are built.

It’s important to note that neural networks can be shallow or deep:

• A shallow network has just one or two hidden layers for simpler tasks. For example, it might help classify emails as spam based on patterns such as sender address and common phrases.

• A deep network has many layers used for more complex problem-solving. For example, it can learn to describe images in full sentences by identifying objects, understanding the context, and generating a caption such as “A child plays soccer on a sunny field.”

Deep learning is a branch of machine learning that relies on deep neural networks that learn complex features on their own, layer by layer. They’re especially useful for working with huge, unstructured datasets such as images, sound, or language.

Key differences

What are the limitations of neural networks?

Neural networks have led to major breakthroughs in AI models, but they also present technical, ethical, and practical challenges. Building AI systems that are not only powerful but also fair, reliable, and sustainable requires understanding of their limitations, such as the following:

High data needs and computing costs: Neural networks—especially deep learning models—require enormous amounts of labeled data. Without it, they can become inaccurate or biased. Training large models also requires powerful computers and a lot of energy, driving up costs for both hardware and electricity.

Lack of transparency: Neural networks are often described as “black boxes.” While they can make accurate predictions, it’s not always clear how they reach their conclusions. That lack of interpretability makes it hard to build trust, especially in critical fields such as healthcare, finance, or public policy.

Example: If a diagnostic system flags a patient for cancer, doctors need to understand why. Without that, the system’s output can raise more questions than answers.

Bias and fairness concerns: Because neural networks learn from data, they also inherit its flaws. If the training data reflects social or historical bias, the model can reproduce or even amplify those patterns. Without careful auditing, this can lead to unfair treatment or discriminatory outcomes.

Example: Some facial recognition systems have been found to perform poorly on people with darker skin tones, leading to real-world harm.

Overfitting and poor generalization: A well-trained neural network should be able to make good predictions on new data, but that’s not always the case. Sometimes models latch onto patterns that don’t hold up outside the training set.

Example: A fraud detection model trained on one region might fail in another because it learned overly specific signals.

Environmental and financial costs: Training large-scale models consumes vast resources. In some cases, training a single language model can produce as much carbon as dozens of roundtrip flights between continents.

Example: Researchers estimated that one major natural language processing model emitted over 600,000 pounds of CO₂—about as much as five cars over their entire lifetimes.

Security and reliability risks: Neural networks can be sensitive to tiny changes in input that might not be noticeable to a human. This opens the door to inaccurate results, where small tweaks can cause big misclassifications.

Example: A stop sign altered with subtle stickers could be misread by an AI system as a yield sign, creating safety risks for autonomous vehicles.

Responsible AI requires developers to carefully consider how they train, apply, and evaluate neural networks. The goal is to make them smarter, safer, fairer, and more trustworthy.

Neural networks at work

Neural networks are changing how industries solve problems, make decisions, and deliver services. Their ability to learn from data, adapt to new inputs, and find patterns in complex systems makes them a strong fit for developing a wide range of applications.

Finance: Fraud detection and forecasting

In banking and investment, neural networks spot unusual patterns that might signal fraud, risk, or opportunity. Their ability to learn from sequences of behavior gives them an edge over traditional rule-based systems.

Examples:

• Fraud prevention: RNNs flag suspicious activity by analyzing transaction patterns and user behavior.

• Forecasting: Models trained on pricing history, sentiment, and economic trends help guide trading and investment decisions.

Benefits:

• Real-time alerts

• More accurate predictions

• Better adaptability to evolving tactics

Healthcare: Diagnostics and patient care

Neural networks are improving how doctors detect diseases, plan treatments, and monitor patients. They process clinical data—such as medical images or electronic health records—to help surface insights and reduce diagnostic delays.

Examples:

• Image analysis: CNNs detect tumors, skin lesions, or lung diseases from medical scans.

• Predictive alerts: Models analyze patient data over time to flag risks of conditions such as heart failure or sepsis.

Benefits:

• Earlier detection and intervention

• Fewer diagnostic errors

• More consistent image reviews

Manufacturing: Quality control and uptime

In industrial settings, neural networks reduce downtime and spot defects in real time. On the factory floor, neural networks help prevent downtime and improve production accuracy.

Examples:

• Predictive maintenance: Models flag signs of wear or failure before breakdowns occur.

• Quality control: Vision systems check for defects at speed and scale.

Benefits:

• Fewer interruptions

• Better product consistency

• Lower maintenance costs

Retail and marketing: Personalization and planning

Retailers use neural networks to tailor recommendations, manage inventory, and forecast demand with precision.

Examples:

• Personalized shopping: Deep learning models suggest products, shows, or ads based on browsing and purchase history.

• Demand planning: Models forecast product needs using seasonal trends, promotions, and external events.

Benefits:

• Higher conversions

• Smarter inventory designs

• More relevant customer engagement

Software development: Saving time and effort

Developers rely on neural networks to streamline a multitude of tasks, including writing and reviewing code, running tests and validating results, and opening pull requests.

Examples:

• New features: Models generate code for APIs, user interfaces, and backend services.

• Debugging: Models quickly identify and resolve memory leaks, performance bottlenecks, and other issues.

• Learning and onboarding: Models provide natural language explanations of codebases or new technologies.

Benefits:

• Automation of tedious tasks

• More time for creative, high-value work

Transportation and logistics: Optimizing the journey

Neural networks support routing, warehouse operations, and vehicle automation. They process real-time data to help systems respond to changes on the ground.

Examples:

• Self-driving tech: Deep neural networks interpret sensor inputs to help vehicles recognize signs, objects, and lane markings in real time.

• Route planning: AI-assisted tools optimize delivery paths, saving time and fuel.

Benefits:

• Faster deliveries

• Reduced fuel costs

• Safer autonomous decisions

GitHub and the future of neural networks

Developers, researchers, and data scientists around the world actively build, refine, and deploy neural networks on GitHub, the world’s largest platform for open-source software development. GitHub plays a central role in the advancement of neural network models by serving as both a code repository and a collaborative space for AI research and deployment.

GitHub repositories host a variety of projects, including neural networks created using deep learning frameworks such as PyTorch, TensorFlow, and Keras. They also host libraries designed for faster neural network model experimentation and scalability, and tools that support interoperability across ecosystems.

Developers choose to save, copy, and improve neural networks on GitHub because it empowers them to:

Build together across teams and borders: GitHub makes it easy for developers and researchers to collaborate in the open. Through pull requests, issues, and documentation, contributors refine models, share ideas, and implement state-of-the-art research.

One standout example of GitHub collaboration is Hugging Face Transformers, a widely used open-source repository for pretrained transformer models used in applications such as translation, summarization, and AI code generation. Its global community of contributors helps expand what's possible with neural networks.

Share and reuse models: Thanks to versioning and built-in support for documentation, GitHub allows developers to share not just code but full neural network models—complete with configuration files, training data links, and usage instructions. This speeds up experimentation and reuse and fits seamlessly into workflows that use version control, documentation, and CI/CD.

One example is a widely adopted object detection project hosted on GitHub. Originally focused on real-time image recognition, it’s now used across fields such as robotics and public safety and is continually improved by researchers and practitioners.

Drive innovation from the ground up: While large companies push the field forward, much of the creativity and momentum comes from individuals experimenting and building on their own. GitHub supports this community with powerful tools and a platform to publish, test, and improve ideas for neural network and other AI models.

Many open-source projects help make neural networks more approachable by turning complex logic into well-documented code. These contributions lower the barrier for newcomers and help more people build real-world solutions.

Help ensure that neural network code is secure: GitHub provides a variety of security tools and features, including features that automatically find and fix vulnerabilities in codebase, and secret scanning to detect sensitive information in code. GitHub also offers guidance and recommendations to help your organization understand its exposure to data leaks.

Build and deploy your neural networks on GitHub

Neural networks are always evolving, and GitHub is where that progress happens. It’s where breakthroughs are replicated, where bugs get fixed in real time, and where new models are tested, refined, and shared by a global community.

Explore ways to more efficiently create neural networks on GitHub using GitHub Copilot, an AI-powered coding assistant that provides intelligent code suggestions, explanations, and automation.

Get started for free.