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backward-propagation

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aadityasikder / Neural-Network-from-Scratch-

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This notebook demonstrates a neural network implementation using NumPy, without TensorFlow or PyTorch. Trained on the MNIST dataset, it features an architecture with input layer (784 neurons), two hidden layers (132 and 40 neurons), and an output layer (10 neurons) with sigmoid activation.

training neural-network numpy evaluation mnist-dataset backward-propagation forward-propagation neural-networks-from-scratch
  • Updated Mar 15, 2024
  • Jupyter Notebook

mattsankner / micrograd

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I build the Micrograd autogradient engine, which is a functioning neural network with forward pass, backward propagation, and stochastic gradient descent, all built from scratch. This is derived from the great @karpathy micrograd lecture. Each notebook is complete with Andrei's lecture code and speech, as well as my own code, anecdotes and addition

machine-learning calculus numpy pytorch neural-networks derivatives stochastic-gradient-descent backward-propagation binaryclassification
  • Updated Jul 21, 2024
  • Jupyter Notebook

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