I got to know Raylib just a few days ago taking a course on learning C++ to start using Unreal Engine. I have a background with assembler(a long time ago), Python/Pygame, C#/Monogame, and Unity/C#. Within the few days I used it, I am simply blown away by the simplicity but yet extremely powerful Raylib library. The routines and functions are very clear and access is very simple. Everything is well documented. I am yet to go in-depth with the library but I never had such an experience in the past building games, which is my main interest. If you stumbled upon this by chance stop and give it a go. You'll never regret it. Right now I am thinking of the many ways I can use this with the languages I know.
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It sounds like you're maybe asking for code frameworks/libraries instead of engines? Something like https://raylib.com/ might be better suited? Source: over 1 year ago
I would recommend SFML or Raylib, they're both excellent and fairly easy to set up, plus have really good documentation. And if you decide to really dig into them you'll eventually be able to create any game you want. Source: over 1 year ago
I'd also recommend raylib as an option. Check out its website: http://raylib.com/. It is beginner friendly enough with good cheatsheet and examples. Source: almost 2 years ago
Finally, you can use raylib.com , a C library but it has a great interface and multiple examples. Howeve, it is not wide-spread like SDL. Source: almost 3 years ago
The easiest option is C# and Unity, even though I think at some point (if you want to experience real programming) you'd better off using a framework. Source: almost 3 years ago
Online Courses: Coursera: "Machine Learning" by Andrew Ng EdX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By... - Source: dev.to / 3 months ago
Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / 12 months ago
The ML component is based on scikit-learn which differentiates it from purely list-based filters. It couples this with a full-featured wireless router (RaspAP) in a single device, so it fulfills the needs of a use case not entirely addressed by Pi-hole. Source: about 1 year ago
Finally, when it comes to building models and making predictions, Python and R have a plethora of options available. Libraries like scikit-learn, statsmodels, and TensorFlowin Python, or caret, randomForest, and xgboostin R, provide powerful machine learning algorithms and statistical models that can be applied to a wide range of problems. What's more, these libraries are open-source and have extensive... Source: about 1 year ago
Scikit-learn is a machine learning library that comes with a number of pre-built machine learning models, which can then be used as python wrappers. Source: about 1 year ago
SFML - SFML provides a simple interface to the various components of your PC, to ease the development of games and multimedia applications. It is composed of five modules: system, window, graphics, audio and network.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Vulkan - Vulkan is a new generation graphics and compute API that provides high-efficiency, cross-platform access to modern GPUs used in a wide variety of devices from PCs and consoles to mobile phones and embedded platforms.
OpenCV - OpenCV is the world's biggest computer vision library
SDL - Simple DirectMedia Layer is a cross-platform multimedia library designed to provide low level...
NumPy - NumPy is the fundamental package for scientific computing with Python