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Neural Networks in Tila ​

This article explains in simple terms what neural networks are, the types available, and how they are used in Tila. No math — just clear analogies, examples, and practical application.

What is a neural network? ​

A neural network is a program that learns to perform tasks: from generating text to recognizing objects in images. Unlike traditional algorithms, a neural network is trained on large amounts of data and adapts to new situations — almost like a human.

In simple terms, it’s a digital “mini-model of the brain” that can write, draw, think, and improve with experience.

Main types of neural networks ​

Tila uses three main categories:

Language Models ​

These are neural networks traditionally called LLMs (Large Language Models) — they process language: answer questions, write texts, summarize documents, help with code. Examples: GPT, Claude, Gemini, and others.

Note:
Sometimes these models are called "text models", but today this term is somewhat outdated. Modern versions — such as GPT-4o and Claude 4 — are already multimodal: they can work not just with text, but also with images, files, and even audio.

Image Generation Networks ​

Create unique images from a description, and can also edit or enhance uploaded pictures. Examples: DALL·E 3, SDXL, Ideogram, Recraft.

Video Generation Networks ​

Generate short videos from text or images. Such models already know how to create movement, change camera angles, and visualize scenes. Examples: Kling, Luma, Minimax.

How are neural networks used in Tila ​

Tila brings neural networks together in a visual workspace. You can:

  • ✍️ Generate texts;
  • 🎨 Create and edit images;
  • 🎥 Generate videos;
  • 📄 Analyze documents and data;
  • 🔊 Transcribe audio or synthesize speech;
  • 🔁 Automate scenarios with agents.

Choosing a model: is it required? ​

Tila automatically selects the optimal neural network for your task so you can focus on the substance, not technical details.

However, if you want more control, you can manually pick the right model. This is especially useful if you want to:

  • Use a specific model’s strengths (e.g., more creative style, better factual accuracy, or code writing ability);
  • Optimize credit usage by choosing a simpler and faster model for basic tasks.

See also:

Conclusion ​

Neural networks aren’t complicated. With Tila, you get access to the latest models in a single window — no need to dig into technical details. Just describe your task, and the result will be ready in moments.