AI: Beginner to Advanced
40 min read
Advanced Neural Networks
Beyond the basics
You've learned the basics of neural networks. Now it's time to go deeper. Advanced architectures, optimization techniques, and scaling laws—these are what separate toy models from production AI systems.
Beyond Simple Networks
Basic neural networks are universal function approximators—in theory, they can learn anything. In practice, architecture matters enormously. Convolutional networks for images. Transformers for sequences. Mixture of experts for scale. The right architecture makes learning easier and inference faster.
Optimization Deep Dive
Training neural networks is an optimization problem: find parameters that minimize loss. But the loss landscape is complex—full of saddle points, local minima, and plateaus. Advanced techniques like Adam, learning rate schedules, and gradient clipping help navigate this landscape effectively.
Scaling Laws
Larger models trained on more data are predictably better. This insight, formalized as "scaling laws," drives modern AI development. Understanding scaling helps you predict model capabilities and make informed decisions about model selection.
💡 Key Takeaways
- Architecture choice dramatically affects learning
- Optimization techniques navigate complex loss landscapes
- Scaling laws predict model capabilities
- These concepts separate hobby projects from production AI
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