Скачать или смотреть Neural Network FROM SCRATCH Using Only Math - No Python, No PyTorch, No Tensorflow

In this video, I build a Neural Network completely from scratch using only math — no Python, no PyTorch, no TensorFlow, no code.

Everything is driven by equations — forward pass, gradients, weight updates — all done manually and explained clearly.

This is the raw essence of how neural networks *actually learn*, without hiding behind high-level libraries.

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⏱️ *Timestamps*
(0:00) Universal Approximation Theorem
(0:09) Intro to the Data
(0:20) Objective of the Video
(1:03) Architecture of the Neural Network
(1:18) Configuration of the Neural Network
(1:39) Backpropagation
(1:52) Batch Gradient Descent Optimization Algorithm
(2:04) Training the Neural Network
(2:25) Forward Pass
(2:44) Computing Loss using MSE
(3:00) Computing Gradients
(3:21) Epoch 1: Parameter Updates
(3:36) Table for Five Epochs
(3:51) Loss Curve Over Epochs
(4:06) Model Performance Comparison
(4:22) Training for More Epochs
(5:00) Neural Network as Universal Function Approximator

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📚 *Further Reading & References*
1. [Neural Networks and Deep Learning – Michael Nielsen](http://neuralnetworksanddeeplearning....)
2. [Simplilearn: What Is a Neural Network?](https://www.simplilearn.com/tutorials...)
3. [GeeksforGeeks — Introduction to Neural Networks](https://www.geeksforgeeks.org/introdu...)
4. [Medium: A Beginner’s Guide to Neural Networks](  / a-beginners-guide-to-neural-networks  )
5. [IBM: Neural Networks Explained](https://www.ibm.com/cloud/learn/neura...)

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