The Math Behind Neural Networks | Explained Visually

Most explanations of AI treat Neural Networks like a "black box," but the true power lies in the mathematics. In this deep dive, we strip away the mystery and explore the actual calculus and linear algebra that allow machines to learn. From single neurons to the intricacies of backpropagation and the chain rule, we’re covering it all—no steps skipped.

What you’ll learn in this video:
- The anatomy of an artificial neuron (Weights, Biases, and Summation).
- Why non-linearity is essential (Sigmoid, ReLU, and Softmax).
- The mechanics of the Forward Pass.
- How we measure error using Loss Functions (MSE).
- Optimizing weights through Gradient Descent and the Learning Rate.
- The "Magic" of Backpropagation and the Chain Rule.

🕒 Timestamps
0:00 - The Biological Inspiration
0:35 - What Is Artificial Neuron? (Weights & Biases)
1:25 - Activation Functions
1:38 - Sigmoid
1:57 - ReLU ( Rectified Linear Unit)
2:17 - Softmax
2:32 - The Forward Pass
3:13 - The Loss Function: Measuring Error
3:50 - Gradient Descent: Finding the Valley
4:25 - Backpropagation & The Chain Rule
5:19 - Summary: From Math to Machine Learning
5:40 - What's Next

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Видео The Math Behind Neural Networks | Explained Visually канала Zero2Algorithm