Understanding The Computational Graph in Neural Networks
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Do you know what is this computational graph used by deep learning frameworks like TensorFlow or PyTorch? The whole logic behind how neural networks function is the back-propagation algorithm. This algorithm allows to update the weights of the network so that it can learn. The key aspect of this algorithm is to make sure we can compute the derivatives or the gradients of very complex functions. That is the whole point of the computational graph! It is to make sure we can backpropagate those derivatives to the whole network, no matter how deep it may be. So Let me show you how it works!
Understanding The Computational Graph in Neural Networks
Understanding The Computational Graph in…
Understanding The Computational Graph in Neural Networks
Do you know what is this computational graph used by deep learning frameworks like TensorFlow or PyTorch? The whole logic behind how neural networks function is the back-propagation algorithm. This algorithm allows to update the weights of the network so that it can learn. The key aspect of this algorithm is to make sure we can compute the derivatives or the gradients of very complex functions. That is the whole point of the computational graph! It is to make sure we can backpropagate those derivatives to the whole network, no matter how deep it may be. So Let me show you how it works!