I use JavaScript and PHP on a daily basis at work. In these languages there are package managers npm (and more) and composer that allow easy dependency management for each project. So far, I've been writing fairly simple scripts in Python and didn't need any package manager. For upcoming projects, I decided to see what the deal is with dependency management in Python.
*This post is about a Starcraft bot I am developing that uses machine learning. The project is being developed as part of the "Daj Się Poznać 2017" contest.
The next few weeks will be spent blogging and creating a project for "Daj Się Poznać 2017." contest. The project requires things I don't know how to do very well yet. That's why I decided that this post will be such an attempt to encompass everything I should focus on in the near future. I really have a lot of bookmarks, especially articles from Arxiv, it is necessary to embrace this mess, make a selection and finally start reading and processing it :)
It's time to get started, the previous post on the "Daj Się Poznać 2017" category was basically a post needed to create the category and RSS feed necessary to participate in the contest. Sculpin, which I use, requires the creation of any post assigned to a category to generate it.
This post is a continuation of a previous post about LabelEncoder. This time it will be about a technique called one hot encoding or one-hot. Having categories converted into corresponding numbers, we can also convert them into several columns (the number of columns depends on how many categories there are), which contain zeros and ones, respectively, denoting whether a row belongs to a category or not. We use this method when we use an algorithm that may have a problem with numeric variables (because they assume some order).
Today I decided to take part in the "Daj Się Poznać 2017" competition organized by Maciej Aniserowicz. The competition involves developing your own Open Source project on GitHub and running a blog by writing a minimum of two posts a week for 10 weeks. The project that I will be developing will be an AI playing Starcraft 2. I just have to count on the fact that by the time the competition starts, i.e. by March 1, DeepMind will provide an API for writing bots, which it is developing in cooperation with Blizzard.
All my competition posts can be found in the tag "DSP2017".
Czasami, przetwarzając zbiór danych, mamy do czynienia ze zmiennymi, które są typu tekstowego i przyporządkowują obserwację statystyczną do jakiejś kategorii. Przykładowo, mamy do czynienia z uczniami pewnej szkoły, którzy chodzą do różnych klas (1A, 1B, 1C, 2A, 2B, 2C itd.). Chcemy takie zmienne zamienić na liczby w celu ich dalszego przetwarzania przez jakiś wybrany algorytm np. random forest. Można do tego użyć klasy LabelEncoder z biblioteki scikit-learn.
Sometimes, when processing a data set, we deal with text-type variables that assign a statistical observation to a category. For example, we are dealing with students of a certain school who go to different classes (1A, 1B, 1C, 2A, 2B, 2C, etc.). We want to convert such variables into numbers for further processing by a selected algorithm, e.g. random forest. You can use the LabelEncoder class from the scikit-learn library for this.
As I am still quite a beginner in the world of Python and its libraries, especially those related to machine learning, many things are unknown to me. An example of this is dividing the data set into parts for training and cross validation. So far, I've done it my own way, but why do it this way when we have other tools for it?
Hello World! Recently, while reading what people write on the #blogreview channel in the Devs PL Slack team, I was inspired to create something of my own. Once upon a time, as part of a competition, I ran a blog for a while, but due to my laziness, the initiative quickly failed. This time I hope that my enthusiasm will last longer.
I will be writing this blog mainly about a field that has recently experienced great growth - machine learning. I'm not particularly advanced, but I'll try not to write nonsense.
Enjoy reading :)