Gradient Descent: The Engine That Made Deep Learning Possible : How one simple idea changed the way machines learn

When I first started learning deep learning, I thought the magic was inside the model architecture. CNNs looked powerful. RNNs looked intelligent. Transformers looked almost impossible to understand. But slowly I realized something important. Architecture is only one part of deep learning. The real question is: How does the model actually learn? A neural network may contain millions or even billions of parameters. But at the beginning, all those parameters are almost useless. They are usually…