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This post provides a comprehensive guide on implementing a neural network from scratch in Rust, using the MNIST dataset for training. Key concepts covered include manual forward and backward propagation, activation functions like ReLU and softmax, one-hot encoding, gradient descent, and accuracy evaluation. The article details the model structure and offers insights on initializing parameters and updating weights.

Neural Network in Rust on MNIST dataset from scratch