First version with training loops.
Features:
- Tensor object
- FeedForward Layer
- Sequential network
- Activation functions: ReLU, LeakyReLU, Sigmoid, Softmax (numerically stable)
- Loss functions: BCE, CrossEntropy, RMSE, BceWithSigmoid, CrossEntropyWithSoftmax
- Autograd solutions for all operations
- Optimizers: SGD, RMSProp
- C++ train loop
- numpy to Tensor and Tensor to numpy conversion
- Compilation into structured Python library
Unit tests:
- Tests for all features in C++ and Python
Examples:
- MNIST dataset learning and verification