Generative neural networks: how they work, what resources they need and why they are so popular
Today we increasingly hear about neural networks that write texts themselves, create unique images and even help invent new drug formulas. Behind this technology stands an entire industry — from giants like Google and OpenAI to startups and small teams that are developing their own products based on generative neural networks. In this article, we, the company EasyByte, will tell you how such models are developed, what computing resources are needed for this, and why they are so impressive. You will also learn how to order the development of a neural network of any complexity from us and how to use a convenient calculator for a preliminary assessment of the project cost.
What are generative neural networks
Generative neural networks — are algorithms capable of creating (generating) new data based on a training set. For example, a generative text neural network is trained on large volumes of text, after which it can write meaningful sentences, articles and even poems itself. And a generative model for images can "draw" pictures from a short description that do not exist in reality, but look like real photographs or paintings.
Why are generative neural networks so popular
- New possibilities. Previously, computer programs could only repeat the actions embedded in them. Generative neural networks, however, create fundamentally new solutions, texts, images.
- High level of creativity. They imitate human imagination, allowing to automate part of creative tasks.
- Convenience for business. Companies around the world use generative models for content marketing, advertising campaigns, design, data analysis — the list is growing every day.
- Progress in hardware. Modern computing power allows training large models that would have been impossible 5-10 years ago due to hardware limitations.
How generative neural networks are created: step by step
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Data Collection. At this stage, we select and structure a dataset necessary for training the model. For text neural networks, this can be millions of articles, books, tweets, comments. For image generating neural networks, — huge databases of images with captions (e.g., "cat on the windowsill", "landscape in an autumn forest").
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Data Preprocessing. Data, whether texts or images, needs to be brought to a common format. Texts are cleaned of extraneous characters, brought to a single encoding, divided into tokens (words/phrases). Images are reduced to a specified resolution, colors are normalized, etc.
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Architecture Selection. There are different types of generative neural networks:
- Generative Adversarial Networks (GAN) — two models (generator and discriminator) that learn by interacting with each other.
- Autoencoders (VAE, Variational Autoencoders) — encode input data into a compressed representation and then reconstruct them back.
- Transformers (Transformer-based models) — architectures that have proven their effectiveness primarily in text processing (GPT, BERT and their modifications), and then they began to be used in image generation tasks.
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Model Training. The most resource-intensive and lengthy stage. During the training process, the model repeatedly "runs" data, selecting internal parameters (neuron weights). For example, large language models can be trained for weeks or even months on supercomputers with hundreds of graphics processors.
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Testing and Optimization. When the model is trained, it is tested on new data that was not used during training. Based on the test results, optimization is carried out: tuning hyperparameters, reducing or increasing the depth of the model, additional training on specific data, etc.
Launch and Integration. The model connects to an application, website, service or any other product. At this stage, it is important to ensure scalability, including proper server configuration, databases, and load distribution.
What power is needed for generative neural networks
Generative neural networks require large volumes of computing resources. The larger the model and the more parameters it has, the higher the requirements for "hardware" — this includes GPU (graphics processors), TPU (tensor processors), and a large amount of RAM. For some modern models, scaling to multiple servers (so-called cluster) is required.
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Text Neural Networks.
- Require large volumes of text data and significant power during training.
- For a ready-made, already trained model (in inference mode), requirements may be lower, but high loads (a large number of users) require powerful servers.
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Neural Networks for Image Generation.
- Training such models requires significant resources — almost always "heavy" GPU equipment is used.
- When generating images from a ready-made model, computing power is also used, but optimization can be achieved, for example, by quantizing the model's weights, compressing data, and fine-tuning the code.
In any case, if your company plans to implement generative neural networks, it is worth thinking ahead about the budget, the required infrastructure, and a scaling scheme.
Application of generative neural networks in business
- Content Marketing and Copywriting. Automatic generation of articles, product descriptions, marketing texts, slogans.
- Design and Advertising. Fast prototypes of advertising banners, unique images for presentations and commercial offers.
- Training and User Support. Chatbots that communicate in natural language and can adapt to the style of a specific audience.
EasyByte: Development of Neural Networks of Any Complexity
EasyByte has been engaged in turnkey neural network development for many years, creating solutions for a wide variety of industries. Our experience allows us to take on projects of any scale — from small prototypes to full-scale corporate platforms:
- Generative Text Models. We can develop a chat bot or a service that generates unique content taking into account the specifics of your audience and corporate style.
- Image Generation Models. Suitable for advertising agencies, designers, game developers — anywhere where it is necessary to quickly obtain original images.
- User Interfaces and Integration. Our team not only writes code and trains models, but also implements them into business processes, configures server infrastructure.
Why Choose Us
- Professionalism. A team of developers, data scientists, and computer vision specialists with many years of experience.
- Individual Approach. We take into account the specifics of your project, workflows, and business goals.
- Flexible Cooperation Terms. You can order a full development cycle or contact us for a consultation, audit, or modification of a ready-made solution.
- Cost Calculator. On our website you will find a convenient calculator that allows you to roughly estimate the budget for creating your neural network.
Conclusions
Generative neural networks are becoming a powerful tool for business today, allowing you to automate creative tasks, improve user experience and create competitive advantages. To create an effective model, you need to think about the architecture, collect high-quality data, allocate sufficient resources for training and properly implement the solution into workflows.
If your company is considering the development of a generative neural network, contact EasyByte — we are ready to create a product of any complexity for you. Try our cost calculator, to estimate how much the development will cost, and contact us for a personalized commercial offer.
EasyByte — your reliable partner in the world of neural networks and artificial intelligence!