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Machine learning has been applied to many problems in cheminformatics and life science, for example, investigating molecular property and developing new drugs. One critical issue in the problem-solving pipeline for these applications is to select a proper molecular representation that featurizes the target dataset and serves the downstream model. Figure…


Image Anomaly Detection appears in many scenarios under real-life applications, for example, examining abnormal conditions in medical images or identifying product defects in an assemble line. In this post, we setup our own case to explore the process of image anomaly detection using a convolutional autoencoder under the paradigm of…


Deep learning for object detection on image and video has become more accessible to practitioners and programmers recently. One reason for this trend is the introduction of new software libraries, for example, TensorFlow Object Detection API, OpenCV Deep Neural Network Module, and ImageAI. These libraries have one thing in common…


a brief review on deep learning in RapidMiner and Orange

I ran into two visual programing environments for data science and machine learning recently: one was RapidMiner and one was Orange. Both systems allow users to build a data science or machine learning solution in a LEGO-like style, i.e., drag-and-drop components to construct a process including data preparation, modeling, evaluation…


All the materials came from the Internet. I am only connecting the dots to make a proof-of-concept implementation.


Orange Data Mining 是一個支援圖形化介面的資料科學及機器學習開發環境, 如下圖所示, Orange 使用人員可以就像玩樂高積木一樣堆疊功能模組進行資料科學及機器學習的資料分析及專案開發。

Orange Data Mining 是由 University of Ljubljana, Slovenia 電腦資訊教學團隊下生醫信息實驗室所開發出來的軟體系統, 系統核心部分支援資料處理、資料可視化、監督式學習、非監督式學習等功能, 系統擴展部分 (add ons) 支援文字處理、圖像辨識、時間序列、模型解釋、生醫信息、單一細胞分析等功能。以下講稿是應醫療界朋友邀約整理的 Orange Data Mining 軟體系統簡介及生醫應用支援, 其中也提到筆者以前寫的兩篇博客: Orange 如何支援深度學習Orange 如何進行肺炎X光圖像檢測

筆者在研究機器學習及深度學習專案時的主要開發環境都是以 Python 編程環境為主, 關注 Orange Data Mining 有兩個目的: 1. 廣泛了解資料科學及機器學習的技術及應用, 2. 持續追蹤圖形化開發環境的適用範圍及設計理念。筆者認為對於剛進資料科學及機器學習相關領域的新鮮人而言, Orange Data Mining 絕對是值得投入時間研究一下的, 對於資深程式開發人員來說, Orange Data Mining 在進行資料分析及開發基礎模型 (benchmark) 兩者上還是有些優點的, 如果各位讀者有任何意見及建議歡迎留言。


台灣人工智慧協會及台北科技大學互動設計系 AI Talk 專題演講


Orange is a visual programming environment for data science and machine learning projects. Under Orange, users can drag-and-drop LEGO-like components to construct a complete solution, including data manipulation/visualization and model building/training/validation for his/her projects. …


列舉十個人工智慧醫療應用資料集的問題說明及解題概要, 資料型態包括數字文字表格資料、時間序列資料、自然語言資料、圖像資料、音頻資料、圖網路資料。

1. 心臟病資料集


Semi-supervised learning is an approach to machine learning that considers both labeled data and unlabeled data in a problem solving process. Semi-supervised learning falls between supervised learning (dealing with labeled data) and unsupervised learning (dealing with unlabeled data). [wiki]

Let’s take image classification under supervised learning and semi-supervised learning as…

franky

PhD, Researcher/Consultant

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