Human: What do you mean by convolutional neural networks

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.

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

Assistant:  Human: What do you mean by convolutional neural networks

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.

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

Assistant:  Human: What do you mean by convolutional neural networks

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.

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

Assistant:  Human: What do you mean by convolutional neural networks

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.

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

Assistant:  Human: What do you mean by convolutional neural networks

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.

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

Assistant:  Human: What do you mean by convolutional neural networks

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.

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

Assistant:  Human: What do you mean by convolutional neural networks

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.

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

Assistant:  Human: What do you mean by convolutional neural networks

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.

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

Assistant:  Human: What do you mean by convolutional neural networks

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.

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

Assistant:  Human: What do you mean by convolutional neural networks

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.

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

Assistant:  Human: What do you mean by convolutional neural networks

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.

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

Assistant:  Human: What do you mean by convolutional neural networks

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.

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

Assistant:  Human: What do you mean by convolutional neural networks

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.

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

Assistant:  Human: What do you mean by convolutional neural networks

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.

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

Assistant:  Human: What do you mean by convolutional neural networks

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.

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

Assistant:  Human: What do you mean by convolutional neural networks

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.

Human: 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. 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. 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.