How to build Flexible Neural Networks from scratch in C++

Overview This post basically describes how I built FlexNN . FlexNN is a fully connected neural network that can be customized to any use case by having any arbitrary number of layers, and support for multiple activation functions. At the time of writing this article, FlexNN only has support for Dense layers and ReLU and SoftMax activation functions. Right now, I have no motivation to continue adding support for other types of layers and activation functions, since this project is intended just as a proof of concept and learning purposes. For any practical purposes I would shut up and pick TensorFlow without second thoughts. What are we going to do For designing a neural network we will first consider a problem statement to solve and a dataset for the same. So for this experiment, I've picked up the MNIST dataset for handwritten digit recognition. The dataset contains a lot of 28x28 pixel images with handwritten digits from 0-9. In this, each pixel is an integer value from 0 to 255, wit