Reinforcement Learning培訓

Reinforcement Learning培訓

在線或現場、由講師指導的實時強化學習培訓課程通過交互式實踐演示如何創建和部署強化學習系統。強化學習培訓可作為“在線實時培訓”或“現場實時培訓”。在線實時培訓(又名“遠程實時培訓”)是通過交互式遠程桌面進行的。現場現場培訓可以在 台灣 中的客戶場所本地進行,也可以在 台灣 中的 NobleProg 公司培訓中心進行。 NobleProg——您當地的培訓提供商

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Reinforcement Learning課程大綱

課程名稱
課程時長
概覽
課程名稱
課程時長
概覽
21小時
This instructor-led, live training in 台灣 (online or onsite) is aimed at developers and data scientists who wish to learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent. By the end of this training, participants will be able to:
  • Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning.
  • Apply advanced Reinforcement Learning algorithms to solve real-world problems.
  • Build a Deep Learning Agent.
21小時
This instructor-led, live training in 台灣 (online or onsite) 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.
21小時
This instructor-led, live training in 台灣 (online or onsite) 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.

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