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Download MNIST dataset to:\n      - TRAIN_IMAGE: `local/mnist/train-images.idx3-ubyte`\n      - TRAIN_LABEL: `local/mnist/train-labels.idx1-ubyte`\n      - TEST_IMAGE: `local/mnist/t10k-images.idx3-ubyte`\n      - TEST_LABEL: `local/mnist/t10k-labels.idx1-ubyte`\n  1.  Run:\n      ```sh\n      cargo test --release -- --include-ignored --nocapture mnist::train\n      ```\n  1.  Inspect parameters at: `local/mnist/params.ron`\n\n## Backpropagation\n\n- distribution of addends of $\\frac{\\partial G}{\\partial f_1}$:\n  ![](img/backpropagation.svg)\n  - a part of the computation graph\n  - $h_i : \\mathbb{R}^{m_i} \\to \\mathbb{R}$\n  - $f_j : \\mathbb{R}^{n_j} \\to \\mathbb{R}$\n  - $h_i$ are the successors of $f_j$\n  - $G$ is the outmost function represented by the root node of the computation graph\n  - $w$ is the tunable parameters of $f_1$\n  - steps:\n    1. nodes $h_1, h_2$ calculate the addends respectively\n    1. nodes $h_1, h_2$ distribute the addends to $f_1, f_2$\n    1. node $f_1$ calculates $\\frac{\\partial G}{\\partial f_1}$ from the received addends\n    1. node $f_1$ calculates $\\frac{\\partial G}{\\partial w}$ using $\\frac{\\partial G}{\\partial f_1}$\n    1. node $f_1$ updates $w$ using $\\frac{\\partial G}{\\partial w}$\n\n## References\n\n- the repo on which mine is based - \u003chttps://github.com/pim-book/programmers-introduction-to-mathematics\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbanyc%2Fneural_network","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbanyc%2Fneural_network","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbanyc%2Fneural_network/lists"}