Description:
State-of-the-art AI: Deep Reinforcement Learning in Python, as the training is published on the Udemy website, and Reinforcement Learning and Deep Learning (Neural Networks) offerings. Science Deep Reinforcement Learning, etc. can be produced robots that even most professionals شطرنجبازهای the world to defeat is. Deep Reinforcement Learning in building artificial intelligence for games such as DOTA 2 and CS:GO is also used. Wrong, only a small part of the capabilities پایانناپذیر Deep Reinforcement Learning Industry.
Our robots, the real world, we have seen how to walk, learn and even after using the simulation, they have learned to improve. One of the things about a good simulation, the lack of the need for hardware, is the real director. You during this period, deliciously, with a lot of examples of artificial intelligence will be familiar.
Advanced AI Features: Deep Reinforcement Learning in Python:
Learn an application of the new algorithm, A2C, i.e. OpenAI baselines
Learning and Evolution Strategies (ES)
Learning and DDPG (Deep Deterministic Policy Gradient)
Better before the start of the course, with the following. be familiar
- Calculation
- Probability
- object-oriented programming
- Python coding: if/else, loops, lists, dicts, sets,
- Numpy coding: matrix and vector operations
- Linear regression
- Gradual descent
- Know how to build a convolutional neural network (CNN) in TensorFlow
- Markov Decision Process (MDP)
Profile course:
Publisher: Udemy
Instructor: Lazy Programmer, Inc.
Number of lessons: 50 lessons in 9 sections
Duration: 8 hours and 32 minutes
Featured course:
Prerequisite course
- Know the basics of MDPs (Markov Decision Processes) and reinforcement learning
-
Helpful to have seen my first two reinforcement learning classes
-
Know how to build a convolutional neural network in Tensorflow
Frame period
Example movie:
Installation guide
After extracting with the player, your custom view.
Subtitles: English
Quality: 720p
Download link
Password file(s): ngaur.com
File size
3.07 GB