|
|
|
|
| LEADER |
02504nmm a2200349 u 4500 |
| 001 |
EB001942863 |
| 003 |
EBX01000000000000001105765 |
| 005 |
00000000000000.0 |
| 007 |
cr||||||||||||||||||||| |
| 008 |
210123 ||| eng |
| 050 |
|
4 |
|a Q325.6
|
| 100 |
1 |
|
|a Tabor, Phil
|
| 245 |
0 |
0 |
|a Reinforcement learning in motion
|c Phil Tabor
|
| 260 |
|
|
|a [Place of publication not identified]
|b Manning Publications
|c 2019
|
| 300 |
|
|
|a 1 streaming video file (5 hr., 56 min., 42 sec.)
|
| 653 |
|
|
|a Machine learning / fast / (OCoLC)fst01004795
|
| 653 |
|
|
|a artificial intelligence / aat
|
| 653 |
|
|
|a Apprentissage automatique
|
| 653 |
|
|
|a Artificial intelligence / http://id.loc.gov/authorities/subjects/sh85008180
|
| 653 |
|
|
|a Reinforcement learning / http://id.loc.gov/authorities/subjects/sh92000704
|
| 653 |
|
|
|a Apprentissage par renforcement (Intelligence artificielle)
|
| 653 |
|
|
|a Artificial intelligence / fast / (OCoLC)fst00817247
|
| 653 |
|
|
|a Reinforcement learning / fast / (OCoLC)fst01732553
|
| 653 |
|
|
|a Machine learning / http://id.loc.gov/authorities/subjects/sh85079324
|
| 653 |
|
|
|a Intelligence artificielle
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
|
|b OREILLY
|a O'Reilly
|
| 500 |
|
|
|a Title from resource description page (Safari, viewed March 13, 2019)
|
| 856 |
4 |
0 |
|u https://learning.oreilly.com/videos/~/10000MNLV201807/?ar
|x Verlag
|3 Volltext
|
| 082 |
0 |
|
|a 000
|
| 082 |
0 |
|
|a 153.15
|
| 520 |
|
|
|a "Reinforcement Learning in Motion introduces you to the exciting world of machine systems that learn from their environments! Developer, data scientist, and expert instructor Phil Tabor guides you from the basics all the way to programming your own constantly-learning AI agents. In this course, he'll break down key concepts like how RL systems learn, how to sense and process environmental data, and how to build and train AI agents. As you learn, you'll master the core algorithms and get to grips with tools like Open AI Gym, numpy, and Matplotlib. Reinforcement systems learn by doing, and so will you in this hands-on course! You'll build and train a variety of algorithms as you go, each with a specific purpose in mind. The rich and interesting examples include simulations that train a robot to escape a maze, help a mountain car get up a steep hill, and balance a pole on a sliding cart. You'll even teach your agents how to navigate Windy Gridworld, a standard exercise for finding the optimal path even with special conditions!"--Resource description page
|