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 seanzhang.me
EM in a nutshell - Ramblings on Deep Learning
| | achyutjoshi.github.io
Machine Learning Takeaways - Following the Feynman Learning Technique
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| | Many would reckon that Machine Learning is now the new oil these days. And I would most likely support that. Personally I got exposed to the world of ML in m...
| | www.hongliangjie.com
Maximum Likelihood as Minimize KL-Divergence - Hong, LiangJie
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| | kyunghyuncho.me
Amortized Mixture of Gaussians (AMoG): A Proof of Concept for "Learning to X", or how I re-discovered simulation-based inference - Kyunghyun Cho
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| | neuralnetworksanddeeplearning.com
Neural networks and deep learning
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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...