Before reading this article, I recommend to have a look at my previous article on TensorFlowKit and like my repository.
When I started working in the field of machine learning, it was quite difficult to move to vectors and spaces from objects and their behavior. At first it was rather complicated to wrap my head around all that, and most processes did not seem obvious and clear at once. That’s the reason why I did my best to visualize everything I did in my groundwork: I used to create 3D models, graphs, diagrams, figures, etc.
When speaking about efficient development of machine learning systems, usually such problems as learning speed control, learning process analysis, gathering various learning metrics, and others are mentioned. The major difficulty is that we (people) use 2D and 3D spaces to describe various processes that take place around us. However, processes within neural networks lay in multidimensional spaces, and that makes them rather difficult to understand. Engineers all around the world understand this problem and try to develop various approaches to the visualization or conversion of multidimensional data into simpler and more understandable forms.