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
Machine Translated
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
Machine Translated
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
概觀
本課程是為管理人員，解決方案架構師，創新人員，CTO，軟件架構師以及對應用人工智能概述及其最近發展預測感興趣的人員而創建的。
14小時
概觀
This instructor-led, live training in 台灣 (online or onsite) is aimed at technical persons who wish to set up or extend an RPA system with more intelligent capabilities.
By the end of this training, participants will be able to:
- Install and configure UiPath IPA.
- Enable robots to manage other robots.
- Apply computer vision to locate screen objects with accuracy.
- Enable robots that can detect language patterns and carry out sentiment analysis on unstructured content.
By the end of this training, participants will be able to:
- Install and configure UiPath IPA.
- Enable robots to manage other robots.
- Apply computer vision to locate screen objects with accuracy.
- Enable robots that can detect language patterns and carry out sentiment analysis on unstructured content.
14小時
概觀
This instructor-led, live training in 台灣 (online or onsite) is aimed at software testers who wish to have an AI driven software testing environment.
By the end of this training, participants will be able to:
- Automate unit test generation and parameterization with AI.
- Apply machine learning learning in a real world use-case.
- Automate the generation and maintenance of API tests with AI.
- Use machine learning methods to self-heal the execution of Selenium tests.
By the end of this training, participants will be able to:
- Automate unit test generation and parameterization with AI.
- Apply machine learning learning in a real world use-case.
- Automate the generation and maintenance of API tests with AI.
- Use machine learning methods to self-heal the execution of Selenium tests.
35小時
概觀
This is a 5 day introduction to Data Science and Artificial Intelligence (AI).
The course is delivered with examples and exercises using Python
The course is delivered with examples and exercises using Python
7小時
概觀
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.
14小時
概觀
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.
21小時
概觀
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
概觀
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
概觀
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
概觀
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
概觀
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
概觀
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
概觀
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。
14小時
概觀
本課程涵蓋Automotive行業的AI（強調Machine Learning和Deep Learning ）。它有助於確定哪種技術（可能）在汽車的多種情況下使用：從簡單的自動化，圖像識別到自主決策。
28小時
概觀
本課程將為您提供神經網絡知識，通常用於機器學習算法，深度學習（算法和應用程序）。
這種訓練更注重基本面，但會幫助你選擇合適的技術： TensorFlow ， Caffe ，泰亞諾，DeepDrive， Keras ，等等這些例子中所作TensorFlow 。
這種訓練更注重基本面，但會幫助你選擇合適的技術： TensorFlow ， Caffe ，泰亞諾，DeepDrive， Keras ，等等這些例子中所作TensorFlow 。
21小時
概觀
該講師指導的實時課程介紹了模式識別和機器學習領域。它涉及統計學，計算機科學，信號處理，計算機視覺，數據挖掘和生物信息學的實際應用。
該課程是互動的，包括大量的動手練習，教師反饋，以及獲得的知識和技能測試。
該課程是互動的，包括大量的動手練習，教師反饋，以及獲得的知識和技能測試。
21小時
概觀
類型：理論培訓與應用程序決定上游與學生在Lasagne或Keras根據教學組
教學方法：演講，交流和案例研究
在破壞了許多科學領域之後，人工智能開始為大量經濟部門（工業，醫學，通信等）帶來革命。然而，它在大媒體中的呈現往往是幻想，遠離Machine Learning或Deep Learning真正領域。此培訓的目的是為已經掌握計算機工具（包括軟件編程基礎）的工程師提供Deep Learning及其各個專業領域的介紹，從而為現有的主要網絡架構提供介紹。今天。如果在課程期間召回數學基礎，建議使用BAC + 2類型的數學水平以獲得更多的舒適度。絕對有可能跳過數學軸只保持“系統”視覺，但這種方法將極大地限制您對該主題的理解。
教學方法：演講，交流和案例研究
在破壞了許多科學領域之後，人工智能開始為大量經濟部門（工業，醫學，通信等）帶來革命。然而，它在大媒體中的呈現往往是幻想，遠離Machine Learning或Deep Learning真正領域。此培訓的目的是為已經掌握計算機工具（包括軟件編程基礎）的工程師提供Deep Learning及其各個專業領域的介紹，從而為現有的主要網絡架構提供介紹。今天。如果在課程期間召回數學基礎，建議使用BAC + 2類型的數學水平以獲得更多的舒適度。絕對有可能跳過數學軸只保持“系統”視覺，但這種方法將極大地限制您對該主題的理解。
7小時
概觀
張量處理單元（TPU）是Google多年來在內部使用的體系結構，現在才變得可供普通大衆使用。它包括幾個專門用于神經網絡的優化，包括簡化的矩陣乘法，以及8位整數而不是16位，以便返回適當的精度級別。 在這個有指導意義的現場培訓中，與會者將學習如何利用TPU處理器的創新優勢，最大限度地提高他們自己的AI應用程序的性能。 在培訓結束時，參與者將能夠： 對大量數據訓練各種類型的神經網絡使用熱塑性聚氨酯加速推理過程高達兩個數量級利用TPU處理密集型應用，如圖像搜索，雲視覺和照片 聽衆 開發商研究人員工程師數據科學家 課程的格式 部分講座，部分討論，練習和沈重的練習
21小時
概觀
Microsoft Cognitive Toolkit 2x（以前的CNTK）是一種開源的商業級工具包，可以訓練深度學習算法，以便像人類大腦一樣學習。根據微軟的說法，CNTK在循環網絡上的速度可能比TensorFlow快510倍，對于與圖像相關的任務來說，TensorFlow的速度要快2-3倍。 在這種有指導性的現場培訓中，參與者將學習如何使用Microsoft Cognitive Toolkit來創建，訓練和評估深度學習算法，以用于涉及多種類型數據（如數據，語音，文本和圖像）的商業級AI應用程序。 在培訓結束後，參與者將能夠： 從Python，C＃或C ++程序中將CNTK作爲庫訪問通過自己的模型描述語言（BrainScript），使用CNTK作爲獨立的機器學習工具。 使用Java程序中的CNTK模型評估功能結合前饋DNN，卷積網絡（CNN）和循環網絡（RNN / LSTM） 在CPU，GPU和多台機器上擴展計算能力使用現有的編程語言和算法訪問海量數據集 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 注意 如果您想定制此培訓的任何部分，包括選擇的編程語言，請聯系我們安排。
21小時
概觀
PaddlePaddle（並行分布式深度學習）是百度開發的可擴展的深度學習平台。 在這個有指導意義的現場培訓中，參與者將學習如何使用PaddlePaddle在其産品和服務應用中實現深度學習。 在培訓結束後，參與者將能夠： 設置並配置PaddlePaddle 建立用于圖像識別和物體檢測的卷積神經網絡（CNN） 爲情感分析設置一個循環神經網絡（RNN） 在推薦系統上建立深度學習，以幫助用戶找到答案預測點擊率（CTR），對大規模圖像集進行分類，執行光學字符識別（OCR），排名搜索，檢測計算機病毒以及實施推薦系統。 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
概觀
浮潛是一個快速創建，建模和管理培訓數據的系統。它側重于加速開發結構化或“黑暗”的數據提取應用程序，該應用程序適用于大型標注培訓集不可用或不易獲取的領域。 在這種有指導意義的現場培訓中，參與者將學習通過使用Snorkel建模訓練數據，從非結構化數據（如文本，表格，圖形和圖像）中提取價值的技術。 在培訓結束後，參與者將能夠： 以編程方式創建訓練集以啓用大量訓練集的標簽通過首先對噪音訓練集進行建模來訓練高質量的最終模型使用呼吸管實施弱監督技術，並將數據編程應用于弱監督的機器學習系統 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
14小時
概觀
Encog是Java和Net的開源機器學習框架。 在這個有指導意義的現場培訓中，參與者將學習先進的機器學習技術，以建立精確的神經網絡預測模型。 在培訓結束後，參與者將能夠： 實施不同的神經網絡優化技術來解決欠擬合和過擬合問題了解並從衆多神經網絡體系結構中進行選擇實施有監督的前饋和反饋網絡 聽衆 開發商分析師數據科學家 課程的格式 部分講座，部分討論，練習和沈重的練習
14小時
概觀
Encog是Java和Net的開源機器學習框架。 在這個有指導意義的現場培訓中，參與者將學習如何使用ENCOG創建各種神經網絡組件。將討論真實世界的案例研究，並探討基于機器語言的解決這些問題的方法。 在培訓結束後，參與者將能夠： 使用標准化過程爲神經網絡准備數據實施前饋網絡和傳播訓練方法實施分類和回歸任務使用Encog基于GUI的工作台對神經網絡進行建模和訓練將神經網絡支持集成到實際應用程序中 聽衆 開發商分析師數據科學家 課程的格式 部分講座，部分討論，練習和沈重的練習
近期人工智能培訓課程
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