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kavita-ganesan.com | ||
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www.v7labs.com
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| | | | | A neural network activation function is a function that is applied to the output of a neuron. Learn about different types of activation functions and how they work. | |
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neuralnetworksanddeeplearning.com
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| | | | | [AI summary] The text provides an in-depth explanation of the backpropagation algorithm in neural networks. It starts by discussing the concept of how small changes in weights propagate through the network to affect the final cost, leading to the derivation of the partial derivatives required for gradient descent. The explanation includes a heuristic argument based on tracking the perturbation of weights through the network, resulting in a chain of partial derivatives. The text also touches on the historical context of how backpropagation was discovered, emphasizing the process of simplifying complex proofs and the role of using weighted inputs (z-values) as intermediate variables to streamline the derivation. Finally, it concludes with a citation and licens... | |
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www.analyticsvidhya.com
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| | | | | Explore RNNs: their unique architecture, working principles, BPTT, pros/cons, and Python implementation using Keras. | |
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sausheong.github.io
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| | | I have written a lot of computer programs in my career, most of the time to solve various problems or perform some tasks (or sometimes just for fun). For most part, other than bugs, as long as I tell the computer what to do very clearly (in whichever the programming language I use) it will obediently follow my instructions. This is because computer programs are really good at executing algorithms - instructions that follow defined steps and patterns that are precise and often repetitious. | ||
