# Mathematics for Artificial Neural Networks

**- Overview **

Artificial Neural Networks (ANNs) combine biological principles with advanced statistics to solve problems in areas such as pattern recognition and gameplay. Artificial neural networks employ a basic model of neuron analogs that are interconnected in multiple ways.

Today, with the help of open source machine learning software libraries such as TensorFlow, Keras or PyTorch, we can create a neural network with very high structural complexity in just a few lines of code. Having said that, the math behind neural networks is still a mystery to some of us, and having knowledge of the math behind neural networks and deep learning can help us understand what's going on inside neural networks. It also helps with architecture selection, fine-tuning of deep learning models, hyperparameter tuning and optimization.

Please refer to **Wikipedia: Mathematics for Artificial Neural Networks** for more details.

**[More to come ...]**