由講師進行實時指導的人工智能本地培訓課程通過動手實踐演示如何實施人工智能解決方案以解決實際問題。
人工智能培訓形式包括“現場實時培訓”和“遠程實時培訓”。現場實時培訓可在客戶位于台灣的所在場所或NobleProg位于台灣的企業培訓中心進行，遠程實時培訓可通過交互式遠程桌面進行。
NobleProg -- 您的本地培訓提供商
客戶評論
他的信息非常豐富，樂於助人。
Pratheep Ravy
課程: Predictive Modelling with R
Machine Translated
它非常互動，比預期更輕鬆和非正式。我們在當時涵蓋了很多主題，培訓師總是樂於接受更詳細的討論，或者更廣泛地討論主題及其相關方式。我覺得培訓給了我繼續學習的工具，相反，它是一次性會議，一旦你完成學習就會停止，這對於主題的規模和復雜性非常重要。
Jonathan Blease
課程: Artificial Neural Networks, Machine Learning, Deep Thinking
Machine Translated
安創造了一個提問和學習的好環境。我們有很多樂趣，同時也學到了很多東西。
Gudrun Bickelq
課程: Introduction to the use of neural networks
Machine Translated
交互式部分，根據我們的特定需求量身定制。
Thomas Stocker
課程: Introduction to the use of neural networks
Machine Translated
我確實喜歡這些練習。
Office for National Statistics
課程: Natural Language Processing with Python
Machine Translated
我真的很喜歡親自動手的方法。
Kevin De Cuyper
課程: Computer Vision with OpenCV
Machine Translated
材料範圍
Maciej Jonczyk
課程: From Data to Decision with Big Data and Predictive Analytics
Machine Translated
系統化ML領域的知識
Orange Polska
課程: From Data to Decision with Big Data and Predictive Analytics
Machine Translated
培訓師知識淵博，包括我感興趣的領域。
Mohamed Salama
課程: Data Mining & Machine Learning with R
Machine Translated
這個話題非常有趣。
Wojciech Baranowski
課程: Introduction to Deep Learning
Machine Translated
培訓師的理論知識和培訓後與參與者解決問題的意願。
Grzegorz Mianowski
課程: Introduction to Deep Learning
Machine Translated
話題。很有意思！。
Piotr
課程: Introduction to Deep Learning
Machine Translated
每個主題後的練習都非常有用，儘管最後太複雜了。一般來說，所提供的材料非常有趣並涉及！圖像識別練習很棒。
Dolby Poland Sp. z o.o.
課程: Introduction to Deep Learning
Machine Translated
我認為，如果培訓是在波蘭語中完成的，那麼培訓師就可以更有效地分享他的知識。
Radek
課程: Introduction to Deep Learning
Machine Translated
深度學習的全球概述。
Bruno Charbonnier
課程: Advanced Deep Learning
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這些練習非常實用，不需要Python的高級知識。
Alexandre GIRARD
課程: Advanced Deep Learning
Machine Translated
使用Eras對實例進行練習。意大利完全理解我們對此培訓的期望。
Paul Kassis
課程: Advanced Deep Learning
Machine Translated
我真的很感激克里斯對我們問題的明確答案。
Léo Dubus
課程: Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
Machine Translated
我一般都很喜歡知識淵博的教練。
Sridhar Voorakkara
課程: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
我對這門課程的標準感到驚訝 - 我會說它是大學標準。
David Relihan
課程: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
非常好的全面概述。 Go OD背景到原因Tensorflow工作，因為它確實。
Kieran Conboy
課程: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
我喜歡有機會提出問題並對理論進行更深入的解釋。
Sharon Ruane
課程: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
我們對這個主題有了更多的了解。我們公司內部的一些真實主題進行了一些很好的討論。
Sebastiaan Holman
課程: Machine Learning and Deep Learning
Machine Translated
通過展示理論與實踐如何相輔相成，培訓提供了正確的基礎，使我們能夠進一步擴展。它實際上讓我對這個主題比以前更感興趣。
Jean-Paul van Tillo
課程: Machine Learning and Deep Learning
Machine Translated
我非常喜歡主題的報導和深度。
Anirban Basu
課程: Machine Learning and Deep Learning
Machine Translated
培訓師很容易解釋困難和高級話題。
Leszek K
課程: Artificial Intelligence Overview
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我喜歡深度機器學習的新見解。
Josip Arneric
課程: Neural Network in R
Machine Translated
我們獲得了一些關於NN的知識，對我來說最有趣的是現在流行的新型NN。
Tea Poklepovic
課程: Neural Network in R
Machine Translated
我最喜歡R :)））中的圖表。
Faculty of Economics and Business Zagreb
課程: Neural Network in R
Machine Translated
關於該主題的培訓師的深刻知識。
Sebastian Görg
課程: Introduction to Deep Learning
Machine Translated
非常更新的方法或CPI（張量流，時代，學習）做機器學習。
Paul Lee
課程: TensorFlow for Image Recognition
Machine Translated
非常靈活。
Frank Ueltzhöffer
課程: Artificial Neural Networks, Machine Learning and Deep Thinking
Machine Translated
我一般都很喜歡靈活性。
Werner Philipp
課程: Artificial Neural Networks, Machine Learning and Deep Thinking
Machine Translated
鑑於技術前景：未來哪種技術/流程可能變得更加重要;看，這項技術可以用於什麼。
Commerzbank AG
課程: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
我從主題選擇中受益。訓練風格。練習方向。
Commerzbank AG
課程: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
都很喜歡
蒙 李
課程: Machine Learning Fundamentals with Python
Machine Translated
教練的指導和舉例
ORANGE POLSKA S.A.
課程: Machine Learning and Deep Learning
Machine Translated
可以自己討論提議的問題。
ORANGE POLSKA S.A.
課程: Machine Learning and Deep Learning
Machine Translated
與講師的交流環節
文欣 张
課程: Artificial Neural Networks, Machine Learning, Deep Thinking
Machine Translated
都喜歡
lisa xie
課程: Artificial Neural Networks, Machine Learning, Deep Thinking
Machine Translated
深入報導機器學習主題，特別是神經網絡。揭開了很多話題的神秘面紗。
Sacha Nandlall
課程: Python for Advanced Machine Learning
Machine Translated
這是我用過的最好的動手編程課程之一。
Laura Kahn
課程: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
Machine Translated
這是我13年職業生涯中最優秀的在線培訓之一。保持偉大的工作！
課程: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
Machine Translated
很多練習，我可以直接在我的工作中使用。
Alior Bank S.A.
課程: Sieci Neuronowe w R
Machine Translated
真實數據的例子。
Alior Bank S.A.
課程: Sieci Neuronowe w R
Machine Translated
神經網絡，循環中的pROC。
Alior Bank S.A.
課程: Sieci Neuronowe w R
Machine Translated
理查德的訓練風格讓它變得有趣，所使用的真實世界的例子有助於將概念帶回家。
Jamie Martin-Royle - NBrown Group
課程: From Data to Decision with Big Data and Predictive Analytics
Machine Translated
內容，因為我覺得非常有趣，並認為這將有助於我在大學的最後一年。
Krishan Mistry - NBrown Group
課程: From Data to Decision with Big Data and Predictive Analytics
Machine Translated
我真的很喜歡練習
L M ERICSSON LIMITED
課程: Machine Learning
Machine Translated
實驗練習
Marcell Lorant - L M ERICSSON LIMITED
課程: Machine Learning
Machine Translated
這是我13年職業生涯中最優秀的在線培訓之一。保持偉大的工作！
課程: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
Machine Translated
AI課程大綱
課程名稱
課程時長
概觀
課程名稱
課程時長
概觀
7小時
This instructor-led, live training in 台灣 (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.
By the end of this training, participants will be able to:
- Understand the basic principles of AlphaFold.
- Learn how AlphaFold works.
- Learn how to interpret AlphaFold predictions and results.
By the end of this training, participants will be able to:
- Understand the basic principles of AlphaFold.
- Learn how AlphaFold works.
- Learn how to interpret AlphaFold predictions and results.
14小時
This instructor-led, live training in 台灣 (online or onsite) is aimed at data analysts and data scientists who wish to use Weka to perform data mining tasks.
By the end of this training, participants will be able to:
- Install and configure Weka.
- Understand the Weka environment and workbench.
- Perform data mining tasks using Weka.
By the end of this training, participants will be able to:
- Install and configure Weka.
- Understand the Weka environment and workbench.
- Perform data mining tasks using Weka.
14小時
The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.
Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
21小時
在這一由講師引導的現場培訓中，參與者將學習Python中最相關及最尖端的機器學習技術，因爲它們構建了一系列涉及圖像、音樂、文本和財務數據的演示應用程序。
在本次培訓結束後，參與者將能夠：
- 運用用于解決複雜問題的機器學習算法和技術
- 將深度學習和半監督學習應用于涉及圖像、音樂、文本和財務數據的應用程序
- 推動Python算法達到其最大潛力
- 使用例如NumPy和Theano的庫和包
受衆
- 開發人員
- 分析師
- 數據科學家
課程形式
- 部分講座、部分討論、練習和大量實操
在本次培訓結束後，參與者將能夠：
- 運用用于解決複雜問題的機器學習算法和技術
- 將深度學習和半監督學習應用于涉及圖像、音樂、文本和財務數據的應用程序
- 推動Python算法達到其最大潛力
- 使用例如NumPy和Theano的庫和包
受衆
- 開發人員
- 分析師
- 數據科學家
課程形式
- 部分講座、部分討論、練習和大量實操
21小時
據估計, 非結構化資料占所有資料的 9 0% 以上, 其中大部分是文本形式的。博客帖子、推特、社交媒體和其他數位出版物不斷增加了這一不斷增長的資料
。
本講師指導的現場課程的核心是從這些資料中提取見解和意義。利用 R 語言和自然語言處理 (NLP) 庫, 我們將電腦科學、人工智慧和計算語言學的概念和技術結合起來, 以演算法方式理解文本資料背後的含義。資料樣本可根據客戶要求提供各種語言版本.
到本培訓結束時, 學員將能夠準備來自不同來源的資料集 (大小), 然後應用正確的演算法分析和報告其意義
課程 的
格式
- 部分講座、部分討論、繁重的動手實踐、偶爾的測試來衡量理解
。
本講師指導的現場課程的核心是從這些資料中提取見解和意義。利用 R 語言和自然語言處理 (NLP) 庫, 我們將電腦科學、人工智慧和計算語言學的概念和技術結合起來, 以演算法方式理解文本資料背後的含義。資料樣本可根據客戶要求提供各種語言版本.
到本培訓結束時, 學員將能夠準備來自不同來源的資料集 (大小), 然後應用正確的演算法分析和報告其意義
課程 的
格式
- 部分講座、部分討論、繁重的動手實踐、偶爾的測試來衡量理解
28小時
本課程的目的是提供在實踐中應用機器學習方法的一般熟練程度。通過使用 Python 程式設計語言及其各種庫, 並基於大量的實際示例, 本課程教授如何使用機器學習最重要的構建塊, 如何做出資料建模決策, 解釋輸出並驗證結果
我們的目標是讓您能夠自信地理解和使用機器學習工具箱中最基本的工具, 並避免資料科學應用的常見陷阱。
我們的目標是讓您能夠自信地理解和使用機器學習工具箱中最基本的工具, 並避免資料科學應用的常見陷阱。
28小時
This course introduces linguists or programmers to NLP in Python. During this course we will mostly use nltk.org (Natural Language Tool Kit), but also we will use other libraries relevant and useful for NLP. At the moment we can conduct this course in Python 2.x or Python 3.x. Examples are in English or Mandarin (普通话). Other languages can be also made available if agreed before booking.
28小時
This is a 4 day course introducing AI and it's application using the Python programming language. There is an option to have an additional day to undertake an AI project on completion of this course.
21小時
Deep Reinforcement Learning refers to the ability of an "artificial agent" to learn by trial-and-error and rewards-and-punishments. An artificial agent aims to emulate a human's ability to obtain and construct knowledge on its own, directly from raw inputs such as vision. To realize reinforcement learning, deep learning and neural networks are used. Reinforcement learning is different from machine learning and does not rely on supervised and unsupervised learning approaches.
In this instructor-led, live training, participants will 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
Audience
- Developers
- Data Scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will 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
Audience
- Developers
- Data Scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14小時
This instructor-led, live training in 台灣 (online or onsite) is aimed at data scientists who wish to use IBM Cloud Pak to prepare data for use in AI solutions.
By the end of this training, participants will be able to:
- Install and configure Cloud Pak for Data.
- Unify the collection, organization and analysis of data.
- Integrate Cloud Pak for Data with a variety of services to solve common business problems.
- Implement workflows for collaborating with team members on the development of an AI solution.
By the end of this training, participants will be able to:
- Install and configure Cloud Pak for Data.
- Unify the collection, organization and analysis of data.
- Integrate Cloud Pak for Data with a variety of services to solve common business problems.
- Implement workflows for collaborating with team members on the development of an AI solution.
28小時
In this instructor-led, live training in 台灣, participants will learn how to implement deep learning models for telecom using Python as they step through the creation of a deep learning credit risk model.
By the end of this training, participants will be able to:
- Understand the fundamental concepts of deep learning.
- Learn the applications and uses of deep learning in telecom.
- Use Python, Keras, and TensorFlow to create deep learning models for telecom.
- Build their own deep learning customer churn prediction model using Python.
By the end of this training, participants will be able to:
- Understand the fundamental concepts of deep learning.
- Learn the applications and uses of deep learning in telecom.
- Use Python, Keras, and TensorFlow to create deep learning models for telecom.
- Build their own deep learning customer churn prediction model using Python.
14小時
嵌入式投影儀是一款開源Web應用程序，用于可視化用于訓練機器學習系統的數據。由Google創建，它是TensorFlow的一部分。 這個有指導意義的現場培訓介紹了嵌入式投影儀背後的概念，並讓參與者通過演示項目的設置。 在培訓結束後，參與者將能夠： 探索機器學習模型如何解釋數據浏覽數據的3D和2D視圖以了解機器學習算法如何解釋它理解嵌入背後的概念及其在表示圖像，單詞和數字的數學向量中的作用。 探索特定嵌入的屬性以了解模型的行爲將嵌入項目應用于真實世界的用例，例如爲音樂愛好者建立歌曲推薦系統 聽衆 開發商數據科學家 課程的格式 部分講座，部分討論，練習和沈重的練習
7小時
This course has been created for managers, solutions architects, innovation officers, CTOs, software architects and anyone who is interested in an overview of applied artificial intelligence and the nearest forecast for its development.
14小時
智能過程自動化（IPA）是指使用Artificial Intelligence (AI) ，機器人技術以及與第三方服務的集成來擴展RPA的功能。
這種由講師指導的實時培訓（現場或遠程）針對希望建立或擴展具有更智能功能的RPA系統的技術人員。
在培訓結束時，參與者將能夠：
- 安裝和配置UiPath IPA。
- 使機器人可以管理其他機器人。
- 應用計算機視覺來精確定位屏幕對象。
- 使機器人能夠檢測語言模式並對非結構化內容進行情感分析。
課程形式
- 互動式講座和討論。
- 很多練習和練習。
- 在現場實驗室環境中動手實施。
課程自定義選項
- 要請求此課程的定制培訓，請與我們聯繫以安排。
- 要了解有關UiPath IPA的更多信息，請訪問：https：// www。 UiPath .com / rpa / intelligent-process-automation
這種由講師指導的實時培訓（現場或遠程）針對希望建立或擴展具有更智能功能的RPA系統的技術人員。
在培訓結束時，參與者將能夠：
- 安裝和配置UiPath IPA。
- 使機器人可以管理其他機器人。
- 應用計算機視覺來精確定位屏幕對象。
- 使機器人能夠檢測語言模式並對非結構化內容進行情感分析。
課程形式
- 互動式講座和討論。
- 很多練習和練習。
- 在現場實驗室環境中動手實施。
課程自定義選項
- 要請求此課程的定制培訓，請與我們聯繫以安排。
- 要了解有關UiPath IPA的更多信息，請訪問：https：// www。 UiPath .com / rpa / intelligent-process-automation
14小時
軟體測試是評估軟體應用程式功能有效性的過程。將人工智慧集成到軟體測試環境中，使該過程能夠以 AI 為驅動，從而加快測試的創作、執行和維護。
此講師指導的現場培訓（現場或遠端）面向希望擁有 AI 驅動的軟體測試環境的軟體測試人員。
培訓結束時，學員將能夠：
- 使用 AI 自動生成和參數化單元測試。
- 在真實用例中應用機器學習學習。
- 使用 AI 自動生成和維護 API 測試。
- 使用機器學習方法自我修復Selenium測試的執行。
課程格式
- 互動講座和討論。
- 大量的練習和練習。
- 在即時實驗室環境中實際實現。
課程自訂選項
- 如需申請本課程的定制培訓，請聯繫我們安排。
此講師指導的現場培訓（現場或遠端）面向希望擁有 AI 驅動的軟體測試環境的軟體測試人員。
培訓結束時，學員將能夠：
- 使用 AI 自動生成和參數化單元測試。
- 在真實用例中應用機器學習學習。
- 使用 AI 自動生成和維護 API 測試。
- 使用機器學習方法自我修復Selenium測試的執行。
課程格式
- 互動講座和討論。
- 大量的練習和練習。
- 在即時實驗室環境中實際實現。
課程自訂選項
- 如需申請本課程的定制培訓，請聯繫我們安排。
7小時
This instructor-led, live training in 台灣 (online or onsite) is aimed at marketers who wish to use AI to improve improve digital marketing strategies through valuable customer insights.
By the end of this training, participants will be able to:
- Leverage AI software to improve the way brands connect to users.
- Use chatbots to optimize the user-experience.
- Increase productivity and revenue through the automation of tasks.
By the end of this training, participants will be able to:
- Leverage AI software to improve the way brands connect to users.
- Use chatbots to optimize the user-experience.
- Increase productivity and revenue through the automation of tasks.
21小時
This instructor-led, live training in 台灣 (online or onsite) is aimed at engineers who wish to program and create robots through basic AI methods.
By the end of this training, participants will be able to:
- Implement filters (Kalman and particle) to enable the robot to locate moving objects in its environment.
- Implement search algorithms and motion planning.
- Implement PID controls to regulate a robot's movement within an environment.
- Implement SLAM algorithms to enable a robot to map out an unknown environment.
By the end of this training, participants will be able to:
- Implement filters (Kalman and particle) to enable the robot to locate moving objects in its environment.
- Implement search algorithms and motion planning.
- Implement PID controls to regulate a robot's movement within an environment.
- Implement SLAM algorithms to enable a robot to map out an unknown environment.
7小時
This instructor-led, live training in 台灣 (online or onsite) is aimed at managers and business leaders who wish to learn about the fundamentals of artificial intelligence and manage AI projects for their organization.
By the end of this training, participants will be able to understand AI at a technical level and strategize using their organization’s data and resources to successfully manage AI projects.
By the end of this training, participants will be able to understand AI at a technical level and strategize using their organization’s data and resources to successfully manage AI projects.
80小時
In this instructor-led, live training in 台灣 (online or onsite), participants will learn the different technologies, frameworks and techniques for programming different types of robots to be used in the field of nuclear technology and environmental systems.
The 4-week course is held 5 days a week. Each day is 4-hours long and consists of lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete various real-world projects applicable to their work in order to practice their acquired knowledge.
The target hardware for this course will be simulated in 3D through simulation software. The code will then be loaded onto physical hardware (Arduino or other) for final deployment testing. The ROS (Robot Operating System) open-source framework, C++ and Python will be used for programming the robots.
By the end of this training, participants will be able to:
- Understand the key concepts used in robotic technologies.
- Understand and manage the interaction between software and hardware in a robotic system.
- Understand and implement the software components that underpin robotics.
- Build and operate a simulated mechanical robot that can see, sense, process, navigate, and interact with humans through voice.
- Understand the necessary elements of artificial intelligence (machine learning, deep learning, etc.) applicable to building a smart robot.
- Implement filters (Kalman and Particle) to enable the robot to locate moving objects in its environment.
- Implement search algorithms and motion planning.
- Implement PID controls to regulate a robot's movement within an environment.
- Implement SLAM algorithms to enable a robot to map out an unknown environment.
- Test and troubleshoot a robot in realistic scenarios.
The 4-week course is held 5 days a week. Each day is 4-hours long and consists of lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete various real-world projects applicable to their work in order to practice their acquired knowledge.
The target hardware for this course will be simulated in 3D through simulation software. The code will then be loaded onto physical hardware (Arduino or other) for final deployment testing. The ROS (Robot Operating System) open-source framework, C++ and Python will be used for programming the robots.
By the end of this training, participants will be able to:
- Understand the key concepts used in robotic technologies.
- Understand and manage the interaction between software and hardware in a robotic system.
- Understand and implement the software components that underpin robotics.
- Build and operate a simulated mechanical robot that can see, sense, process, navigate, and interact with humans through voice.
- Understand the necessary elements of artificial intelligence (machine learning, deep learning, etc.) applicable to building a smart robot.
- Implement filters (Kalman and Particle) to enable the robot to locate moving objects in its environment.
- Implement search algorithms and motion planning.
- Implement PID controls to regulate a robot's movement within an environment.
- Implement SLAM algorithms to enable a robot to map out an unknown environment.
- Test and troubleshoot a robot in realistic scenarios.
120小時
In this instructor-led, live training in 台灣 (online or onsite), participants will learn the different technologies, frameworks and techniques for programming different types of robots to be used in the field of nuclear technology and environmental systems.
The 6-week course is held 5 days a week. Each day is 4-hours long and consists of lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete various real-world projects applicable to their work in order to practice their acquired knowledge.
The target hardware for this course will be simulated in 3D through simulation software. The ROS (Robot Operating System) open-source framework, C++ and Python will be used for programming the robots.
By the end of this training, participants will be able to:
- Understand the key concepts used in robotic technologies.
- Understand and manage the interaction between software and hardware in a robotic system.
- Understand and implement the software components that underpin robotics.
- Build and operate a simulated mechanical robot that can see, sense, process, navigate, and interact with humans through voice.
- Understand the necessary elements of artificial intelligence (machine learning, deep learning, etc.) applicable to building a smart robot.
- Implement filters (Kalman and Particle) to enable the robot to locate moving objects in its environment.
- Implement search algorithms and motion planning.
- Implement PID controls to regulate a robot's movement within an environment.
- Implement SLAM algorithms to enable a robot to map out an unknown environment.
- Extend a robot's ability to perform complex tasks through Deep Learning.
- Test and troubleshoot a robot in realistic scenarios.
The 6-week course is held 5 days a week. Each day is 4-hours long and consists of lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete various real-world projects applicable to their work in order to practice their acquired knowledge.
The target hardware for this course will be simulated in 3D through simulation software. The ROS (Robot Operating System) open-source framework, C++ and Python will be used for programming the robots.
By the end of this training, participants will be able to:
- Understand the key concepts used in robotic technologies.
- Understand and manage the interaction between software and hardware in a robotic system.
- Understand and implement the software components that underpin robotics.
- Build and operate a simulated mechanical robot that can see, sense, process, navigate, and interact with humans through voice.
- Understand the necessary elements of artificial intelligence (machine learning, deep learning, etc.) applicable to building a smart robot.
- Implement filters (Kalman and Particle) to enable the robot to locate moving objects in its environment.
- Implement search algorithms and motion planning.
- Implement PID controls to regulate a robot's movement within an environment.
- Implement SLAM algorithms to enable a robot to map out an unknown environment.
- Extend a robot's ability to perform complex tasks through Deep Learning.
- Test and troubleshoot a robot in realistic scenarios.
14小時
本培訓課程面向希望在實際應用中應用Machine Learning人員。
聽眾
本課程適用於對統計學有一定了解並且知道如何編寫R（或Python或其他選定語言）的數據科學家和統計學家。本課程的重點是數據/模型準備，執行，事後分析和可視化的實踐方面。
目的是為有興趣在工作中應用這些方法的參與者提供Machine Learning實際應用。
行業特定示例用於使培訓與受眾相關。
聽眾
本課程適用於對統計學有一定了解並且知道如何編寫R（或Python或其他選定語言）的數據科學家和統計學家。本課程的重點是數據/模型準備，執行，事後分析和可視化的實踐方面。
目的是為有興趣在工作中應用這些方法的參與者提供Machine Learning實際應用。
行業特定示例用於使培訓與受眾相關。
21小時
人工神經網絡是一種計算數據模型，用於開發能夠執行“智能”任務的Artificial Intelligence (AI)系統。 Neural Networks通常用於Machine Learning （ML）應用程序，它們本身就是AI的一種實現。 Deep Learning是ML的一個子集。
21小時
人工神經網絡是一種計算數據模型，用於開發能夠執行“智能”任務的Artificial Intelligence (AI)系統。 Neural Networks通常用於Machine Learning （ML）應用程序，它們本身就是AI的一種實現。 Deep Learning是ML的一個子集。
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