Building a Feedforward Neural Network from Scratch in C++ | Part 1: Initialization & Setup

In this video, we start building a Feedforward Neural Network (FNN) completely from scratch using C++.

What we cover in Part 1:

Setting up the activation functions (ReLU, Sigmoid)

Creating a simple Matrix class

Initializing network layers, weights, and biases

Using random number generation for weight initialization


This series is perfect if you want a deep understanding of how neural networks work under the hood — without relying on any external libraries!

Stay tuned for Part 2 where we'll implement forward propagation and training logic.


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By the way, I'm not a native English speaker, and I know I sometimes mispronounce words. I'm working on improving, and I'd really appreciate it if you could point out any mistakes to help me get better!

📝 Correction: In this video, I mention “gradient descent,” but more specifically, the algorithm used is stochastic gradient descent (SGD) — since weights are updated after each training example.

Source Code 🧑‍💻 : github.com/rebwarai/Building-a-Feedforward-Neural-…

part2:    • Building a Feedforward Neural Network...  

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