Coding up a Neural Network classifier from scratch – Towards Data Science – Medium

High-level deep learning libraries such as TensorFlow, Keras, and Pytorch do a wonderful job in making the life of a deep learning practitioner easier by hiding many of the tedious inner-working details of neural networks. As great as this is for deep learning, it comes with the minor downside of leaving many new-comers with less foundational understanding to be learned elsewhere. Our goal here is to simply provide a 1 hidden-layer fully-connected neural network classifier written from scratch (no deep learning libraries) to help chip away that mysterious black-box feeling you might have with neural networks. The provided neural network classifies a dataset describing geometrical properties of kernels belonging to three classes of wheat (you can easily replace this with your own custom dataset). An L2-loss function is assumed, and a sigmoid transfer function is used on every node in the hidden and output layers.