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The Loss Landscape | Deep Learning & Gradient Descent

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Navigating the Loss Landscape within deep learning training processes. Variations include: Std SGD, LR annealing, large LR or SGD+momentum. Loss values modified & scaled to facilitate visual contrast.
Explore these animations at the article: The keys of Deep Learning in 100 lines of code: Predict malignancy in cancer tumors with a neural network. Build it from scratch in Python.
As we explore various deep learning architectures, we are re-shaping our loss landscape, transforming it into a smoother or more rugged environment, altering the number of local minima.
And as we change the initialization process we apply to our parameters, we are setting our starting point at different parts of that landscape.
Let's continue exploring diverse ways to navigate the loss landscapes of the most inspiring challenges in the world.
Visuals by Javier Ideami
ideami.com
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