On the second and final part of this conceptual introduction to Machine Learning (ML), I’ll discuss its relationship with other areas (like Data Science) and describe what I perceive as a common theme among many of the ML algorithms. Emphasis on “what I perceive”: don’t take this as the truth.
“Machine Learning” is not just a buzzword — arguably, it is two. Almost everybody seems to be using Machine Learning (ML) in a way or another, and those who aren’t are looking forward to use it. Sounds like a good topic to know about. I did some nice Neural Network stuff with some colleagues in school in the late 90s1. Maybe I could just brag that I have nearly 20 years of experience in the field, but this would not be exactly an honest statement, as I didn’t do much ML since then.
Anyway, this is a fun, useful and increasingly important field, so, I guess it is time to do some ML for real. Here’s the first set of notes about my studies, in which I present some important concepts without getting into specific algorithms.
Once upon a time, Peter John Acklam devised a nice algorithm to approximate the quantile function (AKA inverse cumulative distribution function, or inverse CDF) of the normal distribution. He made the algorithm freely available, but unfortunatelly his page describing it has been timing out for quite a while. So, for reference, here’s a quick overview of his algorithm.
I don’t remember when I created my first homepage, but I know that in 1998 I already had one. It had a mix of content created by myself and those funny things people shared by email back then. I soon dumped all “third-party” content and made my homepage a repository of stuff I created.
This was the philosophical and narcissistic section of my homepage, now converted into a blog post. It has some drops of wisdom uttered by me during the years (from around 1998 until 2012).
Para o deleite de todos, aqui vai minha coleção pessoal de pérolas recolhidas durante campanhas políticas.
I could made this part fit into a single paragraph. Serious, just watch:
Light, textures and shaders are part of the rendering state, so they are handled
just as shown in the previous post.
Sure, using them requires the use of subclasses of osg::StateAttribute that
have not been shown, like osg::Light, osg::Texture2D, and osg::Program,
but the idea is the same. So, just spend some time with the Open Scene Graph
reference documentation and you are done.
“Muito boa noite, amigos! Falamos ao vivo diretamente da Serra Fluminense para a transmissão de uma emocionante e decisiva partida entre Brasil e União Soviética. Este embate é esperado com grande expectativa pela torcida, e não tenho dúvida de que temos grandes emoções pela frente. Junto comigo para esta transmissão, ao meu lado aqui na cabine, Joélson Seicheles. Boa noite, Joélson! O que a torcida brasileira pode esperar do jogo de hoje?”
In the previous part, we talked about two very important OSG concepts: nodes and drawables. Now, we’ll deal with a third very important concept: state sets. These form what I consider the triad of the very fundamental OSG concepts.
Having conceived this text as a practical guide, I was tempted to jump right into action, with an exciting example program displaying some nifty 3D graphics. But, also having conceived this text as something useful for “very beginners”, I resisted this temptation and decided to start with some basic concepts without which the Open Scene Graph (OSG) would not make sense. So, before talking about OSG per se, I’ll start spending a little time with a quite fundamental question.