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sophia
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Neural networks from scratch
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README.md
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# Project Sophia: Make Neural Networks from Scratch ~~Plagiarized from~~ Inspired by [Andrej Karparthy's cs231n](http://cs231n.github.io/neural-networks-case-study/) and [Colah's Blog](http://colah.github.io/posts/2015-08-Backprop/). This notebook recreates neural network algorithms using only the linear algebra library `numpy` (uses other libraries for performance benchmark and visualization). The sample data is a 2-dimensional data with three classes in a form of spirals. The content consists of the following aspects of neural networks: * Weight initialization * Loss functions * Cross-entropy loss * Hinge loss * Regularization loss * Layers * Linear * Sigmoid * Tanh * ReLu * Swish * Softmax * SVM * Dropout * Batch Normalization * Convolution (TBD) * LSTM (TBD) * Embeddings (TBD) * Training * Linear > Softmax * Linear > SVM * [Linear > Activation]++ > [Linear > Output] * Optimization (TBD) * Validation (TBD)
