01 ML experiment – SmartVectors
First experiment of the season with a neural network. Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.
But, using the classic algorithms of machine learning, text is considered as a sequence of keywords; instead, an approach based on semantic analysis mimics the human ability to understand the meaning of a text.
«Machine intelligence is the last invention that humanity
will ever need to make.» Nick Bostrom.
I am going to start a series of videos focused on experiments with machine learning, in this one I want to show you another approach to calculate the smartvector, solving some of the problems we have in the edges, and getting overscan.
I will also show you the result by applying deep learning techniques.
I want to show you the benefits of using a neural network that allows us to perform the same process of inpaint, but in a much more accurate way and with much better results.
In this case I am going to use this flow-edge neural network, I will leave you all the links but you have it available on their GitHub to download and run directly on your computers.
But I’m going to use a much simpler option, which is a google collab notebook, these notebooks allow us to run python code, in this case our neural network without having to install anything. You can use this link to use the google collab notebook.
in my github repository you can find the nuke templates that I have used in the explanation of the video, and this is what you will find.