在線或現場，講師指導的實時計算機視覺培訓課程通過互動式討論和動手實踐演示計算機視覺的基礎知識，參與者逐步創建簡單的電腦視覺應用。
計算機視覺培訓以「在線實時培訓」或「現場實時培訓」的形式提供。在線實時培訓（又名“遠程實時培訓”）通過互動式
NobleProg -- 您當地的培訓機構
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Testimonials
他給我們的例子。
JONATHAN MARIANO, si
Course: Artificial Intelligence (AI) for Managers
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實驗
JONATHAN MARIANO, si
Course: Artificial Intelligence (AI) for Managers
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介紹的練習和範例。
Marcos - JONATHAN MARIANO, si
Course: Artificial Intelligence (AI) for Managers
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機器勒寧的主題。
Víctor Edgar - JONATHAN MARIANO, si
Course: Artificial Intelligence (AI) for Managers
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老師的態度
Ivonne Guadalupe Avendaño Hernandez - JONATHAN MARIANO, si
Course: Artificial Intelligence (AI) for Managers
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所教授的概念清晰，實用，並且有助於瞭解如何使用AI和ML這個主題。
Miguel - JONATHAN MARIANO, si
Course: Artificial Intelligence (AI) for Managers
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講師的經驗和知識。
SERGIO BRAVO - JONATHAN MARIANO, si
Course: Artificial Intelligence (AI) for Managers
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也許是一些練習。
Hilario García - JONATHAN MARIANO, si
Course: Artificial Intelligence (AI) for Managers
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做法之一
JONATHAN MARIANO, si
Course: Artificial Intelligence (AI) for Managers
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教師學科的知識和處理
Zaira N. - JONATHAN MARIANO, si
Course: Artificial Intelligence (AI) for Managers
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創新，因為它是我們已經生活的東西。
jesus isaias - JONATHAN MARIANO, si
Course: Artificial Intelligence (AI) for Managers
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Computer Vision子類別
Computer Vision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 intermediate to advanced-level developers, researchers, and data scientists who wish to learn how to implement real-time object detection using YOLOv7.
By the end of this training, participants will be able to:
- Understand the fundamental concepts of object detection.
- Install and configure YOLOv7 for object detection tasks.
- Train and test custom object detection models using YOLOv7.
- Integrate YOLOv7 with other computer vision frameworks and tools.
- Troubleshoot common issues related to YOLOv7 implementation.
14小時
SimpleCV是一個開源框架 - 意味著它是一組可用於開發視覺應用程序的庫和軟件。它允許您處理來自網絡攝像頭，Kinects，FireWire和IP攝像頭或移動電話的圖像或視頻流。它可以幫助您構建軟件，使您的各種技術不僅可以看到世界，還可以了解它。 聽眾 本課程面向尋求使用SimpleCV開發計算機視覺應用的工程師和開發人員。
21小時
Caffe是一個深刻的學習框架，以表達，速度和模塊化為基礎。 本課程以MNIST為例，探討了Caffe作為圖像識別的深度學習框架的應用 聽眾 本課程適合有興趣使用Caffe作為框架的Deep Learning研究人員和工程師。 完成本課程後，代表們將能夠：
- 了解Caffe的結構和部署機制
- 執行安裝/生產環境/架構任務和配置
- 評估代碼質量，執行調試，監控
- 實施高級生產，如培訓模型，實施圖層和日誌記錄
14小時
Marvin is an extensible, cross-platform, open-source image and video processing framework developed in Java. Developers can use Marvin to manipulate images, extract features from images for classification tasks, generate figures algorithmically, process video file datasets, and set up unit test automation.
Some of Marvin's video applications include filtering, augmented reality, object tracking and motion detection.
In this instructor-led, live course participants will learn the principles of image and video analysis and utilize the Marvin Framework and its image processing algorithms to construct their own application.
Format of the Course
- The basic principles of image analysis, video analysis and the Marvin Framework are first introduced. Students are given project-based tasks which allow them to practice the concepts learned. By the end of the class, participants will have developed their own application using the Marvin Framework and libraries.
14小時
計算機視覺是一個涉及從數字媒體自動提取、分析和理解有用信息的領域。Python是一種高級編程語言，因其清晰的語法和代碼可讀性而聞名。
在這一由講師引導的現場培訓中，學員將使用Python逐步創建簡單的計算機視覺應用程序，並從中習得計算機視覺的基礎知識。
在本次培訓結束後，學員將能夠：
- 了解計算機視覺的基礎知識
- 使用Python來實現計算機視覺任務
- 使用Python構建自己的計算機視覺應用程序
- 對計算機視覺感興趣的Python程序員
- 部分講座、部分討論、練習和大量實操
21小時
深度學習是機器學習的一個子領域。它使用基於學習數據表示的方法和諸如神經網絡的結構。 Keras是一種用於快速開發和實驗的高級神經網絡API。它運行在TensorFlow ， CNTK或Theano CNTK 。 這種以講師為主導的現場培訓（現場或遠程）是針對希望使用深度學習技術構建自動駕駛汽車的開發人員。 在培訓結束時，參與者將能夠：
- 使用計算機視覺技術識別車道。
- 使用Keras構建和訓練卷積神經網絡。
- 訓練深度學習模型以區分交通標誌。
- 模擬完全自動駕駛的汽車。
- 互動講座和討論。
- 大量的練習和練習。
- 在實時實驗室環境中親自實施。
- 要申請本課程的定制培訓，請聯繫我們安排。
14小時
This instructor-led, live training in 台灣 (online or onsite) is aimed at developers who wish to build hardware-accelerated object detection and tracking models to analyze streaming video data.
By the end of this training, participants will be able to:
- Install and configure the necessary development environment, software and libraries to begin developing.
- Build, train, and deploy deep learning models to analyze live video feeds.
- Identify, track, segment and predict different objects within video frames.
- Optimize object detection and tracking models.
- Deploy an intelligent video analytics (IVA) application.
7小時
This instructor-led, live training in 台灣 (online or onsite) is aimed at back-end developers and data scientists who wish to incorporate pre-trained YOLO models into their enterprise-driven programs and implement cost-effective components for object-detection.
By the end of this training, participants will be able to:
- Install and configure the necessary tools and libraries required in object detection using YOLO.
- Customize Python command-line applications that operate based on YOLO pre-trained models.
- Implement the framework of pre-trained YOLO models for various computer vision projects.
- Convert existing datasets for object detection into YOLO format.
- Understand the fundamental concepts of the YOLO algorithm for computer vision and/or deep learning.
14小時
模式匹配是一種用於在圖像中定位指定模式的技術。它可用於確定捕獲圖像中指定特徵的存在，例如工廠生產線中缺陷產品上的預期標籤或組件的指定尺寸。它不同於“ Pattern Recognition ”（它識別基於較大的相關樣本集合的一般模式），因為它具體決定了我們正在尋找的東西，然後告訴我們預期的模式是否存在。
- 本課程介紹了模式匹配領域中使用的方法，技術和算法，因為它適用於Machine Vision 。
28小時
OpenCV (Open Source Computer Vision Library: http://opencv.org) is an open-source BSD-licensed library that includes several hundreds of computer vision algorithms.
Audience
This course is directed at engineers and architects seeking to utilize OpenCV for computer vision projects
21小時
這個有指導意義的實時培訓介紹了從零開始構建面部識別系統所需的軟件，硬件和分步驟過程。面部識別也被稱爲人臉識別。 本實驗中使用的硬件包括Rasberry Pi，相機模塊，舵機（可選）等。參與者負責自行購買這些組件。使用的軟件包括OpenCV，Linux，Python等。 在培訓結束後，參與者將能夠： 在Rasberry Pi上安裝Linux，OpenCV和其他軟件實用程序和庫。 配置OpenCV以捕獲和檢測面部圖像。 了解將Rasberry Pi系統打包用于實際環境的各種選項。 使系統適應各種用例，包括監視，身份驗證等。 聽衆 開發商硬件/軟件技術人員所有行業的技術人員愛好者 課程的格式 部分講座，部分討論，練習和沈重的練習 注意 其他硬件和軟件選項包括：Arduino，OpenFace，Windows等。如果您想使用其中的任何一種，請聯系我們安排。
14小時
This instructor-led, live training in 台灣 (online or onsite) is aimed at software engineers who wish to program in Python with OpenCV 4 for deep learning.
By the end of this training, participants will be able to:
- View, load, and classify images and videos using OpenCV 4.
- Implement deep learning in OpenCV 4 with TensorFlow and Keras.
- Run deep learning models and generate impactful reports from images and videos.
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