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GOBLIN | Dynamics of the Loss Landscape of Gradient Descent | Deep Learning

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GOBLIN takes us on a journey, in training mode, from above the edge horizon of the loss landscape of a convnet, during its training process, through the edge horizon (laterally) and to the perspective from below its dynamic convexity.
More details and related analysis about this and other visualizations will be published in the future.
Loss Landscape generated with real data: convnet, imagenette dataset, sgd-adam, bs=16, bn, lr sched, train mod, 500k pts, 0.5 w range, log scaled (orig loss nums) & vis-adapted
trained with the awesome fast.ai library
In the intersection between research and art, the A.I LL project explores the morphology and dynamics of the fingerprints left by deep learning optimization training processes.
The project goes deep into the training phase of these processes and generates high quality visualizations, using some of the latest deep learning and machine learning research and producing inspiring animations that can both inform and inspire the community.
As the weight space changes through the optimization process, loss landscapes become alive, organic entities that challenge us to unlock the mysteries of learning.
How do these multidimensional entities behave and change as we modify hyperparameters and other elements of our networks?
How can we best tame these wild beasts as we cross their edge horizon on our way to the deepest convexity they hold?
losslandscape.com
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