v0.9.0

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

v0.1.0

Computational graph on Tensors completed