Gradient Descent: The Engine That Made Deep Learning Possible : How one simple idea changed the way machines learn
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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…
1Key Takeaways
- When I first started learning deep learning, I thought the magic was inside the model architecture.
- Transformers looked almost impossible to understand.
- But slowly I realized something important.
- Architecture is only one part of deep learning.
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that when I first started learning deep learning, I thought the magic was inside the model architecture.
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