NN#9 — Neural Networks Decoded: Concepts Over Code

Author(s): RSD Studio.ai

Originally published on Towards AI.

Convergence Assurance Techniques for Modern Deep Learning

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When we talk about neural networks, we often fixate on the architecture — how many layers, what activation functions, the number of neurons.

But just as a race car’s performance depends on more than its engine, a neural network’s success hinges on much more than its basic structure. That’s where convergence assurance techniques come in — the sophisticated methods that guide our networks toward optimal solutions with greater efficiency, reliability and performance.

Think about learning to ride a bicycle. At the beginning, you wobble tremendously, overcorrect, and perhaps fall. With practice, your adjustments become more subtle, more precise — you converge toward balanced riding. Neural networks face a similar journey.

Without proper guidance, a neural network might never find its balance. It could oscillate wildly around the optimal solution, take an eternity to reach it, or get stuck in a suboptimal state altogether.

Convergence techniques are our tools to ensure that doesn’t happen.

These convergence techniques are of various types. Some are done through modifying neural network architecture and other are done through changing hyperparameters. Let’s look at them one by one!

Imagine you’re building a skyscraper. The foundation needs different properties than… Read the full blog for free on Medium.

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Published via Towards AI