Dozentengeführte Reinforcement Learning Präsenzschulungen in Schweiz.
Erfahrungsberichte
Ich mag Beispiele zum Erklären
AUO友达光电（苏州）有限公司
Kurs: OptaPlanner in Practice
Machine Translated
Reinforcement Learning Kurspläne
Name des Kurses
Dauer
Überblick
Name des Kurses
Dauer
Überblick
21 hours
Überblick
Reinforcement Learning (RL) is a machine learning technique in which a computer program (agent) learns to behave in an environment by performing the actions and receiving feedback on the results of the actions. For each good action, the agent receives positive feedback, and for each bad action, the agent receives negative feedback (penalty).
This instructor-led, live training (onsite or remote) is aimed at data scientists who wish to go beyond traditional machine learning approaches to teach a computer program to figure out things (solve problems) without the use of labeled data and big data sets.
By the end of this training, participants will be able to:
- Install and apply the libraries and programming language needed to implement Reinforcement Learning.
- Create a software agent that is capable of learning through feedback instead of through supervised learning.
- Program an agent to solve problems where decision making is sequential and finite.
- Apply knowledge to design software that can learn in a way similar to how humans learn.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
This instructor-led, live training (onsite or remote) is aimed at data scientists who wish to go beyond traditional machine learning approaches to teach a computer program to figure out things (solve problems) without the use of labeled data and big data sets.
By the end of this training, participants will be able to:
- Install and apply the libraries and programming language needed to implement Reinforcement Learning.
- Create a software agent that is capable of learning through feedback instead of through supervised learning.
- Program an agent to solve problems where decision making is sequential and finite.
- Apply knowledge to design software that can learn in a way similar to how humans learn.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
21 hours
Überblick
Reinforcement Learning (RL) is an area of AI (Artificial Intelligence) used to build autonomous systems (e.e., an "agent") that learn by interacting with their environment in order to solve a problems. RL has applications in areas such as robotics, gaming, consumer modeling, healthcare, supply chain management, and more.
This instructor-led, live training (onsite or remote) is aimed at data scientists who wish to create and deploy a Reinforcement Learning system, capable of making decisions and solving real-world problems within an organization.
By the end of this training, participants will be able to:
- Understand the relationships and differences between Reinforcement Learning and machine learning, deep learning, supervised and unsupervised learning.
- Analyze a real-world problem and redefine it as Reinforcement Learning problem.
- Implementing a solution to a real-world problem using Reinforcement Learning.
- Understand the different algorithms available in Reinforcement Learning and select the most suitable one for the problem at hand.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
This instructor-led, live training (onsite or remote) is aimed at data scientists who wish to create and deploy a Reinforcement Learning system, capable of making decisions and solving real-world problems within an organization.
By the end of this training, participants will be able to:
- Understand the relationships and differences between Reinforcement Learning and machine learning, deep learning, supervised and unsupervised learning.
- Analyze a real-world problem and redefine it as Reinforcement Learning problem.
- Implementing a solution to a real-world problem using Reinforcement Learning.
- Understand the different algorithms available in Reinforcement Learning and select the most suitable one for the problem at hand.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Zukünftige Reinforcement Learning Kurse
2020-06-17 09:30:00
Zürich
21 hours
2020-06-22 09:30:00
Basel
21 hours
2020-08-19 09:30:00
Basel
21 hours
2020-09-02 09:30:00
Zürich
21 hours
2020-12-01 09:30:00
Zürich
21 hours
Other countries
Consulting
Reinforcement Learning Schulung, Reinforcement Learning boot camp, Reinforcement Learning Abendkurse, Reinforcement Learning Wochenendkurse, Reinforcement Learning Kurs, Reinforcement Learning Training, Reinforcement Learning Seminar, Reinforcement Learning Seminare, Reinforcement Learning Privatkurs, Reinforcement Learning Coaching, Reinforcement Learning Lehrer
_ireland.gif "environmental protection agency (epa) ireland")
.jpg "american international group (aig)")
.png "european central bank (ecb)")
.png "aeronautical radio, incorporated (arinc)")
.jpg "european molecular biology laboratory (embl)")
