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www.lesswrong.com | ||
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www.khanna.law
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| | | | | You want to train a deep neural network. You have the data. It's labeled and wrangled into a useful format. What do you do now? | |
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blog.vstelt.dev
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| | | | | [AI summary] The article explains the process of building a neural network from scratch in Rust, covering forward and backward propagation, matrix operations, and code implementation. | |
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www.paepper.com
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| | | | | [AI summary] This article explains how to train a simple neural network using Numpy in Python without relying on frameworks like TensorFlow or PyTorch, focusing on the implementation of ReLU activation, weight initialization, and gradient descent for optimization. | |
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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... | ||
