Convolutional Neural Networks (CNN): An AI Perspective
Convolutional neural networks, or CNNs, have become a breakthrough in computer vision. Their architecture makes it possible to effectively recognize images, be it faces in photographs, objects in videos, or defects in industrial photographs. The principle of CNN operation is based on feature extraction: the model first looks for simple elements like lines, then combines them into more complex shapes. It is thanks to CNN that facial recognition systems in smartphones and autopilots that detect road signs work. They process information in the same way as the human eye, but much faster and more accurately.
Recurrent neural networks (RNN): memory for text and speech
Recurrent neural networks, or RNNs, are distinguished by their ability to process sequential data. Unlike CNNs, they “remember” previous steps, which is critical for working with text, speech or time series. RNNs are used to translate languages, automatically create video subtitles, generate meaningful sentences, and even predict stock prices. Essentially, these models read and write, grasping context, just like a human does. Understanding their mechanics opens the way to creating your own tools for analyzing and generating text.
Generative Models: Creating New Things
Generative models are AI that can create entirely new content. These include GANs (generative adversarial networks) and diffusion models. If you have seen realistic images of non-existent people or animals created by AI, this is the work of such systems. They are used for design, creating artwork, generating realistic data to train other models, and even synthesizing music. These neural networks learn from huge amounts of data and are then able to reproduce their style, but with new content, opening up horizons for creativity and innovation.
Transformers and Large Language Models (LLM)
Transformers is a modern architecture that has enabled incredible results in natural language processing. It is on the basis of transformers that large language models (LLMs) are created that we use every day. Their peculiarity is the “attention” mechanism, which allows the model to focus on the most important parts of the input data, regardless of their position. LLMs are capable of conducting dialogue, writing articles, translating, summarizing and even creating code. Understanding the operating principles of transformers is the key to effectively using and configuring modern AI assistants for a wide variety of tasks.
How to choose the type of AI for your task
Choosing the right type of neural network depends on your specific task. If you need to analyze images or videos, look towards CNN. If you work with text, speech or time series, RNNs or transformers will be more effective. To generate unique content, pay attention to generative models. At LearnAI, we teach not only the theory, but also the practice of using these models so that you can confidently use AI assistants to solve real-life problems. Master this knowledge so you can not just use the tools, but also understand why they work the way they do.