𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗳𝗿𝗼𝗺 𝗦𝗰𝗿𝗮𝘁𝗰𝗵: 𝗡𝗼 𝗣𝘆𝗧𝗼𝗿𝗰𝗵 & 𝗧𝗲𝗻𝘀𝗼𝗿𝗙𝗹𝗼𝘄. 𝗝𝘂𝘀𝘁 𝗽𝘂𝗿𝗲 𝗺𝗮𝘁𝗵

Beneath the surface of AI that feels like magic, lies elegant mathematics and careful coding. For those who love to learn deeper, 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: https://www.youtube.com/watch?v=A83BbHFoKb8&feature=youtu.be

𝗙𝗶𝗿𝘀𝘁 𝟯𝟬 𝗠𝗶𝗻𝘂𝘁𝗲𝘀: 𝗧𝗵𝗲𝗼𝗿𝘆 𝗮𝗻𝗱 𝗠𝗮𝘁𝗵
This segment goes into the equations and logic behind neural networks.

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: 𝗣𝘆𝘁𝗵𝗼𝗻 𝗰𝗼𝗱𝗶𝗻𝗴
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. We will use NumPy, allowing us to manipulate arrays and matrices while staying close to the mathematical essence of neural networks.

You will get a "high" if your from-scratch code works, and you see that you can make good predictions on the dataset.

In a world where high-level libraries handle the heavy lifting, you might wonder, “Why bother with this hard way? I can do all of this in 10 lines of code which ChatGPT can give me.” 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 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. 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:

Let’s make AI a little less magical and a lot more understandable.

Let me know your thoughts after watching.