Building a Neural Network from Scratch: A Deep Learning Project with Source Code

 Building a neural network from scratch is an exciting deep-learning project. It allows you to gain a better understanding of how neural networks work and allows you to create your own custom models that can be used for various tasks.  With the right resources and guidance, building a neural network from scratch can be both a rewarding and educational experience. 

The first step in this project is understanding what exactly makes up a neural network. Neural networks are composed of layers that contain neurons that process data by passing it through weights or biases to generate outputs based on their inputs. The connections between neurons form pathways known as edges which allow them to communicate with each other within the same layer or across different layers depending on their configuration. Once these concepts have been understood, one must then decide what type of model they would like to build such as convolutional or recurrent networks, both having distinct advantages over traditional feed-forward architectures when dealing with complex datasets such as images or text respectively. 

After deciding on what kind of model will best suit your needs, it’s time for coding! Depending on whether Python, C++, Java etc.. was chosen earlier in development; there are many open source libraries available online (such as TensorFlow) containing functions necessary for creating nodes/neurons along with optimizing hyperparameters like learning rate & batch size etc. After all, components have been properly implemented into the code; it's time for testing! This involves running several tests against sample datasets while monitoring accuracy/error rates until desired results have been achieved. If successful; congratulations! You just created your very own custom-built Neural Network from Scratch!