In the previous article about our journey into the world of AI, a small smattering was given on the most common artificial intelligences (textual ones, in particular) and their learning methods, but how does the graphical learning of artificial intelligences happen?
The graphical learning of artificial intelligences
There are so many graphical learning methods of artificial intelligences, here we will briefly analyze 14 of the most common; it should be noted that not all of these machine learning types are unique to AI graphical learning, but these are the ones used for it.
1. Generative Adversarial Networks (GANs).
The GANs they are a type of neural network made up of two parts: a generator and a discriminator. The generator creates new data, such as images or sounds, while the discriminator tries to distinguish the data generated by the generator from the real ones. The training of the GANs leads the generator to constantly improve the quality of the generated data until they become difficult to distinguish from the real ones.
2. Neural Style Transfer, Neural Style Transfer
This technique graphics learning machine learning allows you to transfer the artistic style of an image (such as a famous painting) to another image, while preserving the content; for you to understand you could make a Monet painting be generated in Leonardo da Vinci style or vice versa.
3. Deep Dream, Deep Dream
Deep Dream, on the other hand, is an image processing approach that uses neural networks to generate psychedelic and dream-like images; this type of graphic learning attempts to maximize the activation of certain neurons within the network to create bizarre and fascinating images, along the lines of those of futurism to understand each other.
4. Variational Autoencoders (VAEs) , Variational Autoencoder
THE VAEs are neural networks that learn to encode input data in a low-dimensional latent space and then decode it to reconstruct the original data. They are used for the generation of new data similar to the input data, for example in the generation of realistic human faces.
5. Image-to-Image Translation, Image-by-Image Translation
This guy of AI translates one image of one domain into another; for example, it can be used to convert a line drawing into a realistic photo or to change the color of an image while keeping its contents intact.
6. Super Resolution, Super Resolution
There super resolution has as its focal point to improve the resolution of a low resolution image, making it clearer and more detailed. Neural networks are trained to learn the relationship between low- and high-resolution images, allowing lost detail to be reconstructed; long story short, you know when in movies you see a blurry image become sharper? Here you are.
7. Deep Reinforcement Learning
The Deep Reinforcement Learning it is a form of machine learning in which an agent learns to make decisions by taking actions in an environment to maximize a reward. It is often used in games and autonomous robots.
8. Deep Convolutional Neural Networks (CNNs).
The CNNs they are a type of neural network used for image processing and pattern recognition. They are designed to automatically capture image features across their convolutional layers.
9. Object Detection
L’object detection uses CNNs to find and locate specific objects within an image; it is used in applications such as video surveillance and autonomous driving.
10. Image Segmentation, Image Segmentation
The graphical learning technique known as Image Segmentation it is based on dividing an image into different “areas” to simplify the analysis of the contents and the processing of information, in practice it creates a sort of mosaic with several images in a nutshell.
11. Natural Language Processing (NLP) for Image Understanding
This type of learning artificial intelligence graphic uses natural language to “understand” and describe image content; to make you understand, it can generate text descriptions of scenes or objects detected in an image that a hypothetical bot “sees”.
12. Transfer Learning
The Transfer Learning involves using pre-trained AI models on large datasets as a starting point for specific tasks. This approach reduces training time and improves model performance in data-limited tasks.
13. Recurrent Neural Networks (RNNs).
The RNNs are neural networks specialized in processing sequential data, such as natural language. They are able to capture long term dependencies in data sequences.
14. Attention Mechanisms
The Attention Mechanisms allow models to focus on specific parts of the input data, ignoring less relevant information. They are widely used in neural networks to improve performance in complex tasks.
In summary
The 14 types of Artificial Intelligence described above regarding the graphic learning of artificial intelligences are nothing more than a small smattering of the many powerful applications of AI (at least in the visual field); it should be noted that they are very complex concepts and all 14 of them deserve an article to be fully understood, but this is not the place to talk about them individually, one by one, in depth.
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