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.
List of 51 TensorFlow deep learning tutorial videos. TensorFlow™ is an open source software library for numerical computation using data flow graphs....
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
Hi Guys, I have Always been asked to share my code which I use in my video. Answering people’s questions is great, and the feeling you get when you solve a p...
Learn the Linux/ Unix command line (Bash) with our 13 part beginners tutorial. Clear descriptions, command outlines, examples, shortcuts and best practice.
- 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
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?