Journey to the depths of the Loss Landscape, visualizing the morphology & dynamics of deep learning training processes, beginning rollout at , phase 1 completed, phase 2 ongoing already with more stuff coming up in future
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?
Exploring the morphology and dynamics of gradient descent within deep learning optimization processes, we go deep into the "minds" of artificial neural networks.
LL is led by Javier Ideami, researcher, multidisciplinary creative director, engineer and entrepreneur. Contact Ideami on [email protected]
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?
Exploring the morphology and dynamics of gradient descent within deep learning optimization processes, we go deep into the "minds" of artificial neural networks.
LL is led by Javier Ideami, researcher, multidisciplinary creative director, engineer and entrepreneur. Contact Ideami on [email protected]
- Kategori
- Belgesel
Yorum yazmak için Giriş yap ya da Üye ol .
Henüz yorum yapılmamış. İlk yorumu siz yapın.