prompter: What do you mean by convolutional neural networks

assistant: Convolutional Neural Networks (CNNs) are a type of deep neural network that are mainly used for image recognition and classification tasks. They are composed of multiple layers of neurons, including convolutional layers, pooling layers, and fully-connected layers. Convolutional layers apply various filters to the input images and extract features from them. Pooling layers reduce the size of the feature maps and help to reduce the number of parameters in the network. Fully-connected layers connect all the neurons in the network and allow them to learn complex patterns from the input data.

assistant: A Convolutional Neural Network (CNN) is a type of artificial neural network used in image recognition and processing that is specifically designed to process pixel data. It uses convolutional layers to scan the image, extract features, and reduce the dimensionality of the image. The extracted features are then passed through multiple layers to make a prediction. CNNs have proven to be effective in a variety of image-related tasks such as image classification, object detection, and image segmentation.

assistant: Convolutional neural networks are a sub-type of neural networks. They require inputs that are ordered in one or more convolution dimensions, like samples in audio data in 1-D or pixels in images in 2-D. Convolutional neural networks process the data in a translationally equivariant way, i.e. a shift in the input data along the convolution dimension results in a corresponding shift of the outputs along that dimension. Internally they use a shared-weight architecture of the convolution kernels or filters that slide along input features.

prompter: What are the application of such algorithms, mention top 3 use cases

prompter: I didn't understand any of that. Can you rephrase it in simple terms for me? Use an image as an example.

assistant: Sure, let me explain it in simpler terms. Convolutional neural networks are a type of artificial intelligence used for image recognition. Imagine you have a picture and you want a computer to identify what's in it. A convolutional neural network works by breaking down the image into small parts, or "features," and analyzing each feature to identify patterns. The computer then uses those patterns to determine what's in the image as a whole.

assistant: Imagine you're trying to identify objects in an image. A regular neural network might just look at each individual pixel and try to make sense of it. But with a convolutional neural network, it would look at small sections of the picture at a time, and use the same set of rules to process each of those sections. This way, it can identify objects even if they're in different parts of the picture, because it has learned to recognize the features of an object regardless of where they are. These features are found using a set of filters that scan the picture, and these filters are used on every section to identify objects.

assistant: In simple terms, there's nothing magical about it, it's all math.
There is a normalized "input" variable that can range from -1.0 to 1.0, each of them being a "rank" to determine the result, that is, the more negative for -1, the more chances of the result being A, the more positive for the 1, the more are the chances of the result being B.

The calculation consists of a series of complex operations that can return a magic number called "weight", the final choice is determined from the sum of the "input" with the "weight" .

For example, if the input is 2 and the weight is -0.5, the result will be 1.5, which, if we set 0 as the threshold between the two choices, we will get B.

The bias is also just a number added to the weight.

The complex operations to generate the weight occur on top of the dataset, in this way the final weight (which is not something magical, it is literally a number) is like a "footprint" of the results of the dataset, the learned thing.

prompter: What other types of neural networks are there? Also, what type of networks do LLMs use?

prompter: Could you give me a practical example as to when this is used?

assistant: Sure, a practical example of the use of convolutional neural networks is in image classification. For instance, given a set of labeled images of objects such as cats, dogs, cars, etc., a convolutional neural network can be trained to identify which object is in a new, unseen image. The network does this by learning to recognize key features of the objects in the training images, such as edges, corners, textures, and so on, which are then detected in the new image and used to make a prediction.

assistant: Yes, a common practical application of convolutional neural networks is in image classification, where the network is trained to identify objects in an image. Other examples include speech recognition, video classification, and natural language processing.