- A collection of tools for working with planning domains
- api.planning.domains
- solver.planning.domains
- editor.planning.domains
- education.planning.domains
Top Journals For Geometry And Topology with Impact Factor, Citescore, Overall Ranking/Rating, h-index, SCImago Journal Rank (SJR), ISSN, Publisher, and other Important Details
All AI/Machine learning jobs in one place. Machine learning jobs; remote, on location, interesting companies, and a directory of developers that work with machine learning
Tungsteno is a project whose goal is to make mathematics accessible to everybody, completely free, based on open collaboration and the best pedagogical tools
Databases are the cornerstone of any Software Applications. You will need one or more databases to develop almost all kind of Software Applications: Web, Enterprise, Embedded Systems, Real-Time…
Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. Flexible Data Ingestion.
This page contains list of mathematical Theorems which are at the same time (a) great, (b) easy to understand, and (c) published in the 21st century. See here for more details about these criteria. Click on any theorem to see the exact formulation, or click here for the formulations of all theorems. You can also…
"In computer science, syntactic sugar is syntax within a programming language that is designed to make things easier to read or to express. It makes the language "sweeter" for human use: things can be expressed more clearly, more concisely, or in an alternative style that some may prefer." -- https://en.wikipedia.org/wiki/Syntactic_sugar
A curated list of awesome Rust Swift iOS Android Python Java PHP Ruby C++ JavaScript .Net Nodejs Go Golang Linux React Vue frameworks, libraries, software and resourcese
A collection of 800+ resources for developers presented in curated lists. Learn programming, find a new job, discover your next favourite podcast, improve your workflow and a lot more.
This site contains many samples and prototypes that I have developed. All the code for the samples are available on bitbucket and people are free to do with it what they will. Ideally its best if you use the latest version of the Chrome browser, I tend to use the latest available APIs and features as I prototype and learn new subjects.
* 2D Prototypes
* 3D Fungi Prototypes
* SDF / Ray Marching Prototypes
* Three.js Prototypes
* 3D Shader Prototypes
* UI Web Components
* Misc
These are articles about the techniques I develop and lessons I learnt while toying or working with computer graphics. Most of it is self-taught and there's lots of reinventing the wheel (which I recommend) but also some innovative and new discoveries that often times are not documented anywhere else (and if any of this content becomes part of your paper or the center of your PhD thesis, I feel it'd be fair to mention this website).
- Modern C++ for Computer Vision
- 3D Coordinate Systems
- Photogrammetry I
- Mobile Sensing and Robotics I
- Photogrammetry II
- Mobile Sensing and Robotics II
- Techniques for Self-Driving Cars
- Master Project
This program aims to reunite researchers across disciplines that have played a role in developing the theory of reinforcement learning. It will review past developments and identify promising directions of research, with an emphasis on addressing existing open problems, ranging from the design of efficient, scalable algorithms for exploration to how to control learning and planning. It also aims to deepen the understanding of model-free vs. model-based learning and control, and the design of efficient methods to exploit structure and adapt to easier environments.
The program focused on the following four themes:
- Optimization: How and why can deep models be fit to observed (training) data?
- Generalization: Why do these trained models work well on similar but unobserved (test) data?
- Robustness: How can we analyze and improve the performance of these models when applied outside their intended conditions?
- Generative methods: How can deep learning be used to model probability distributions?