Building a Neural Network from Scratch Using Pytorch
This is it, the Grand Finale. 🚀 We’ve spent four episodes mastering the individual components of AI, but now we’re pulling the trigger. In this episode, we walk line-by-line through a complete training loop to build a Titanic Survivor Predictor that hits 83% accuracy.
Forget the "Black Box." We are building the engine from the ground up using nothing but pure PyTorch tensors and our own logic.
What we’re covering in this deep dive: ✅ The Multi-Layer Setup: Moving from a single linear model to a Neural Network with hidden layers. ✅ Matrix Multiplication (@): Demystifying the symbol that powers the modern world. ✅ Activation Functions: Why we use ReLU to give our model "personality" and Sigmoid for probabilities. ✅ The Training Loop: A line-by-line breakdown of the Forward Pass, the Backward Pass, and the Weight Update. ✅ Validation vs. Training: Why we test our AI on data it’s never seen before to prove it actually learned.
If you’ve ever wanted to know exactly what is happening inside the code of a Neural Network, this video is the ultimate guide.
Chapters: 0:00 The Grand Finale Intro 1:15 Initializing Multi-Layer Coefficients 3:30 Matrix Multiplication & ReLU Explained 5:45 The Forward Pass 7:20 The "Magic" of .backward() 9:10 Updating Weights with torch.no_grad() 11:30 Reaching 83% Accuracy (Validation) 13:45 Series Wrap-Up: What's Next?
Resources: 🔗 Titanic Dataset (Kaggle): [https://www.kaggle.com/competitions/t...]
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