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What Are Convolution Neural Networks? [ELI5] | HackerNoon

Universal Approximation Theorem says that Feed-Forward Neural Network (also known as Multi-layered Network of Neurons) can act as powerful approximation to learn the non-linear relationship between the input and output. But the problem with the Feed-Forward Neural Network is that the network is prone to over-fitting due to the presence of many parameters within the network to learn.



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What Are Convolution Neural Networks? [ELI5] | HackerNoon

https://hackernoon.com/-understanding-convolution-neural-networks-cnn-the-eli5-way-photo-by-efe-kurnaz-on-unsplash-u-pa1i327j

Universal Approximation Theorem says that Feed-Forward Neural Network (also known as Multi-layered Network of Neurons) can act as powerful approximation to learn the non-linear relationship between the input and output. But the problem with the Feed-Forward Neural Network is that the network is prone to over-fitting due to the presence of many parameters within the network to learn.



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https://hackernoon.com/-understanding-convolution-neural-networks-cnn-the-eli5-way-photo-by-efe-kurnaz-on-unsplash-u-pa1i327j

What Are Convolution Neural Networks? [ELI5] | HackerNoon

Universal Approximation Theorem says that Feed-Forward Neural Network (also known as Multi-layered Network of Neurons) can act as powerful approximation to learn the non-linear relationship between the input and output. But the problem with the Feed-Forward Neural Network is that the network is prone to over-fitting due to the presence of many parameters within the network to learn.

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      Universal Approximation Theorem says that Feed-Forward Neural Network (also known as Multi-layered Network of Neurons) can act as powerful approximation to learn the non-linear relationship between the input and output. But the problem with the Feed-Forward Neural Network is that the network is prone to over-fitting due to the presence of many parameters within the network to learn.
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      Universal Approximation Theorem says that Feed-Forward Neural Network (also known as Multi-layered Network of Neurons) can act as powerful approximation to learn the non-linear relationship between the input and output. But the problem with the Feed-Forward Neural Network is that the network is prone to over-fitting due to the presence of many parameters within the network to learn.
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