Introduction to Neural Networks (Lecture 24)

Описание: Welcome to the twenty-fourth lecture of my Deep Learning series! 🧠📉

In the previous 23 lectures, we painstakingly built our own deep learning library (Micrograd) from scratch, creating the Engine (backpropagation) and the Neural Network modules. Today, all that hard work pays off. In this video, we train our very first Neural Network!

This lecture is the bridge between building the architecture and making it intelligent. We will take the MLP class we built last time and implement the fundamental "Training Loop"—the iterative process that allows a network to learn from data.

We are solving a simple regression problem, treating the network as a binary classifier to predict target values of 1.0 or -1.0.

In this video, we cover:
✅ The Dataset: We define a tiny dataset of 3-dimensional inputs (xs) and their corresponding desired targets (ys). This simple setup allows us to trace every number in the network.
✅ The Forward Pass: We feed our data into the MLP to get the initial (random) predictions.
✅ The Loss Function (MSE): We implement Mean Squared Error manually. We calculate the difference between what the network predicted and what we wanted, square it (to handle negatives and penalize large errors), and sum it up. This single number represents how "bad" our network currently is.
✅ Zeroing Gradients: A critical step! We explain why gradients accumulate in Micrograd (and PyTorch) and why we must reset them to zero (zero_grad()) before every backward pass.
✅ The Backward Pass: We call loss.backward() to calculate the gradients for every single weight and bias in the network with respect to the loss.
✅ Gradient Descent (The Update): This is the heart of learning. We iterate through all parameters and slightly nudge them in the opposite direction of the gradient (data -= learning_rate * grad) to minimize the loss.
✅ The Training Loop: We wrap all these steps into a loop, running for 1,000 "epochs." We watch the loss dramatically drop from ~2.9 to nearly 0.0 as the network "learns" the data.
✅ Saving the Model: Finally, we show how to use Python's pickle/dill library to save our trained model weights to a file and reload them later for inference.

By the end of this lecture, you will have witnessed the complete lifecycle of a deep learning model: Data → Model → Loss → Backprop → Update. We have successfully overfitted a small dataset, proving our library works!

Resources:
🔗 GitHub Repository (Code & Notes):
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Now that we've trained a network in our own library, in the next few lectures, we will compare this side-by-side with PyTorch to see how professional production code looks, and dive deeper into binary classification and decision boundaries. Let's keep building! 📉🚀

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