Can you give me a high level overview over how Convolutions work in Neural Networks? I know about Sobel filters and understand how they work, but I don't know how/whether the idea translates to Convolutions. I know a fair bit of Linear Algebra and also understand fully connected layers and activation functions, if that is relevant for the explanation.
Sure, I'd be happy to give you a high-level overview of how convolutions work in neural networks.

Convolutional neural networks (CNNs) use a specific type of layer called a convolutional layer, which performs convolutions on the input data. A convolution is a mathematical operation that combines two functions to produce a third function that expresses how one of the original functions modifies the other. In the case of CNNs, the convolutional layer applies a set of filters to the input data, which convolves the filters with the input to produce a set of output feature maps.

Here's a high-level overview of how convolutions work in CNNs:

1. Convolutional filters: A convolutional layer uses a set of filters (also called kernels or weights) to extract specific features from the input data. These filters are small matrices of weights that are learned during the training process.

2. Sliding window: The filters slide across the input data in a specific pattern (typically in a sliding window manner), computing the dot product between the filter values and the corresponding input values at each location. This dot product operation produces a single value in the output feature map, which corresponds to the activation of the filter at that location.

4. Stride and padding: The stride determines the step size of the sliding window as it moves across the input, while padding can be added to the edges of the input to ensure that the output feature maps have the same spatial dimensions as the input.

5. Non-linear activation: The output of the convolutional layer is typically passed through a non-linear activation function, such as ReLU, to introduce non-linearity into the network.

6. Pooling: After convolution, a pooling layer is often used to downsample the output feature maps, reducing their spatial dimensions and introducing some level of translation invariance. Common pooling operations include max pooling and average pooling.

7. Overall, convolutions allow the network to learn a hierarchy of increasingly complex features by building up from simple patterns to more complex structures. The first layer may learn low-level features such as edges, while subsequent layers learn more abstract features such as object parts and textures.

I hope that helps! Let me know if you have any further questions.
No further questions thank you!
You're welcome! I'm glad I could help. If you have any questions in the future, don't hesitate to ask. Have a great day!
Thank you, I hope you have a great day as well.