An Introduction To Mathematics Behind Neural Networks

Today, with open source machine learning software libraries such as TensorFlow, Keras, or PyTorch we can create a neural network, even with high structural complexity, with just a few lines of code. Having said that, the mathematics behind neural networks is still a mystery to some of us, and having the mathematics knowledge behind neural networks and deep learning can help us understand what's happening inside a neural network. It is also helpful in architecture selection, fine-tuning of deep learning models, hyperparameters tuning, and optimization. I had ignored understanding the mathematics behind neural networks and deep learning for a long time as I didn't have good knowledge of algebra or differential calculus. A few days ago, I decided to start from scratch and derive the methodology and mathematics behind neural networks and deep learning, to know how and why they work.