- Understanding the GitHub Flow
- Hello World
- Getting Started with GitHub Pages
- Git Handbook
- Forking Projects
- Be Social
- Making Your Code Citable
- Mastering Issues
- Mastering Markdown
- Documenting your projects on GitHub
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This book explains the algorithms behind those collisions using basic shapes like circles, rectangles, and lines so you can implement them into your own projects.
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?
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.
Learn the Linux/ Unix command line (Bash) with our 13 part beginners tutorial. Clear descriptions, command outlines, examples, shortcuts and best practice.
A collection of .BLEND and .FBX files to accompany the Robotic Design with Blender tutorial series on YouTube:(Part 1) https://youtu.be/aRBHMRa6pIA(Part 2) https://youtu.be/TKc-g84j2x8(Part 3) https://youtu.be/Cuo_ytkvCpo(Part