Build a Neural Network from Scratch with NumPy | No Libraries, Just Code
Learn how to implement a complete neural network from scratch using only Python and NumPy — no TensorFlow or PyTorch! In this step-by-step tutorial, we take the math and diagrams from Part 1 and turn them into fully working code inside a Jupyter Notebook.
🚀 What You’ll Learn:
How forward propagation and backpropagation work in practice
ReLU and Sigmoid activation functions in NumPy
Binary cross-entropy loss function
Manual weight updates — no libraries, full control
How to train and predict with a basic neural network
📌 Perfect for machine learning beginners, students, or anyone who wants to truly understand how neural networks work under the hood.
🔔 Don’t forget to like, comment, and subscribe for Part 3 — where we build a handwritten digit classifier using NumPy.