Unit-4 Non convex Optimizations |19AD602Deep Learning |SNS INSTITUTIONS

Описание: HI ALL THIS VIDEO IS A VIDEO ON THE TOPIC NON CONVEX OPTIMIZATION
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Non-convex optimization involves finding the minimum (or maximum) of a function that is not convex, meaning it can have multiple local minima and maxima. This makes the optimization process challenging, as algorithms may get stuck in suboptimal solutions. In deep learning, most loss functions are non-convex due to the complex structure of neural networks with multiple layers and non-linear activations. Despite this, methods like stochastic gradient descent (SGD) and its variants often find good enough solutions that generalize well in practice. Understanding and navigating non-convex landscapes is crucial for training deep models effectively and achieving high performance.