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 sebastianraschka.com
Losses Learned
| | jxmo.io
Introduction to variational autoencoders - Jack Morris
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| | A primer on variational autoencoders (VAEs) culminating in a PyTorch implementation of a VAE with discrete latents.
| | dennybritz.com
Implementing a Neural Network from Scratch in Python ยท Denny's Blog
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| | All the code is also available as an Jupyter notebook on Github.
| | 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...
| | blog.vstelt.dev
Writing a Neural Network from Scratch in Rust
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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.