Neural Network From Scratch: Pure Math Approach Without Pytorch & TensorFlow | 30 Min Theory + 30 Min Coding

Join our "Neural Network from Scratch" course with lecture videos, hand-written notes, assignments, certificate, community support and lifetime access here: https://vizuara.ai/courses/neural-network-from-scratch/ Here is the Google Colab Notebook link: https://colab.research.google.com/drive/1jFB6r_prNCRH0kiuTqy1Lfgrndk38GKD?usp=sharing Train.csv file link: https://drive.google.com/file/d/1kobc4-1jZlOBOZBf52NNDPgosnuJD84S/view?usp=drive_link Miro handwritten notes for this lecture: https://miro.com/app/board/uXjVLzJhIHU=/?share_link_id=163362306356 ___________________________________________________ "Building a Neural Network from Scratch: A Journey into Pure Math and Code" But beneath the surface of AI that feels like magic, lies elegant mathematics and careful coding. For those of us who love peeling back the layers, there's something incredibly satisfying about building a neural network from scratch—not using PyTorch or TensorFlow packages, but with nothing but pure mathematics. If you have ever wanted to fully understand deep neural networks, this is your opportunity. I have created a 1-hour video on Vizuara’s YouTube channel, where we will understand and implement a neural network step-by-step. The Breakdown of the Video First 30 Minutes: Theory and Math This segment dives deep into the equations and logic behind neural networks. Whether it’s calculating the gradients or dissecting the role of the loss function, you’ll understand the "why" behind every concept. 1) We start with the fundamentals—no shortcuts, no pre-built libraries. 2) Problem statement and dataset 3) Defining the neural network architecture by hand 3) Setting up forward propagation 4) What exactly is backpropagation, and why is it so central to deep learning? 5) Setting up the mathematical equations for gradient descent. Next 30 Minutes: Coding in Python Once we have laid the theoretical foundation, we move to implementation. Here, you will see every line of code written from scratch—initializing weights, performing forward and backward passes, and updating the parameters using gradient descent. NumPy serves as our toolkit, allowing us to manipulate arrays and matrices while staying close to the mathematical essence of neural networks. You will definitely get a high if your from scratch code works and you see that you are able to make good predictions on the dataset. In a world where high-level libraries handle the heavy lifting, you might wonder, “Why bother with the hard way? I can do all of this in 10 lines of code.” Here’s why: 1) Deep understanding: Pre-built frameworks are powerful but abstract. Writing your own neural network forces you to understand how each component works together. 2) Debugging: When something goes wrong in a complex model, having a firm grasp of the fundamentals can save hours of frustration. 3) Foundational skills: Learning to code from scratch builds confidence and lays a solid foundation for more advanced topics like custom layers, optimizers, and model architectures. This lecture is perfect for anyone curious about AI and machine learning—whether you are just starting out or looking to strengthen your foundational knowledge. You don’t need an extensive math background, just a willingness to learn and follow along. If this sounds like something you would enjoy, check out the full video on Vizuara’s YouTube channel. By the end, you will have your very own neural network running—not because a library did it for you, but because you built it with your own hands. Watch the full video here: https://youtu.be/A83BbHFoKb8 Let’s make AI a little less magical and a lot more understandable. Let me know your thoughts after watching.