The Loss Landscape high resolution collection pieces are created with real data from the training processes of artificial deep neural networks (AI) through a complex process composed of multiple stages and contain the highest resolution and quality representations in the world of the surfaces that express the performance of the learning processes of artificial neural networks. In these pieces you are looking at the fingerprints of the learning process of artificial machines, the shadows of spaces that have billions of dimensions, spaces navigated by neural networks that are looking for the right combination of their parameters. To create this piece I slice the very high dimensional space to capture a dimensionality reduced representation that preserves the essence of its key features, combining engineering, scientific and creative disciplines to produce, through a number of complex stages, the final piece in very high detail and resolution, in a process that requires a lot of time, computation, effort and creativity. Because of the difficulty of the creation of these pieces in high resolution and detail, there are very few available. Loss Landscape created with data from the training process of a convolutional network. Convnet, imagenette dataset, sgd-adam, bs=16, bn, lr sched, train mod, 250k pts, 20p-interp, log scaled (orig loss nums) & vis-adapted. When analyzing the loss landscape generated while increasing dropout, we see a noise layer taking over the landscape, a layer that is disruptive enough to help in preventing overfitting and the memorization of paths and routes across the landscape, and yet not disruptive enough to prevent convergence to a good minima (unless dropout is taken to extreme values).