Backpropagation Explained: Why Write Gradients by Hand?

Backpropagation is the core idea that makes neural networks learn — but most tutorials hide what is actually happening behind PyTorch, TensorFlow, or other deep learning libraries.

In this video, I break down backpropagation from scratch and explain how gradients flow through a neural network, why we calculate derivatives, and how the model updates its weights during training.

You’ll learn:
✅ What backpropagation actually means
✅ How gradients help neural networks learn
✅ Why writing gradients by hand makes deep learning easier to understand
✅ What PyTorch autograd is doing behind the scenes
✅ How the math connects to real neural network training

If you are learning machine learning, deep learning, neural networks, or PyTorch, this video will help you build a much stronger foundation.

Watch till the end to understand the math that PyTorch hides.

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Видео Backpropagation Explained: Why Write Gradients by Hand? канала MAVERICK