Microsoft recommendation solution on azureDuran Hsieh
It is a big-data and AI workshop in National Yunlin University of Science and Technology. I have two sessions this workshop:
1. SEO (2hours)
2. Microsoft recommendation solution on Azure (3hours)
I introduce what is data mining, recommendation system, and Azure. I also show:
1. Participant how to deploy recommendation solution to azure
2. How to use azure storage
3. How to use recommendation solution
Mealionaire: A context-aware and ontology-based mobile recommender system for...Carter Chen
The night markets and the small eats in Taiwan are famous all over the world, eating is the big thing in Taiwan. No matter what time is it, people here in Taiwan can go outdoors, accessing vendors and get something to eat. Although those food vendors and restaurants provide a great variety of fresh food for people to choose, people have a hard time to make decisions sometimes. In this study, we have proposed a mobile recommender system, which is called Mealionaire. The recommender system is context-aware, which means it can perceive the context around the user and the recommendations it produced will adapt to the context whenever the context changes. In addition, it also introduces ontology technologies to analyze the relations between the user, the restaurants and the dishes which are served by the restaurants, in order to provide tailored, personalized results for the user in real-time fashion. The evaluation results show that Mealionaire can provide high user satisfaction, and it is capable to solve the problems that users may face when they are looking for appropriate dishes to eat in their daily lives.
Microsoft recommendation solution on azureDuran Hsieh
It is a big-data and AI workshop in National Yunlin University of Science and Technology. I have two sessions this workshop:
1. SEO (2hours)
2. Microsoft recommendation solution on Azure (3hours)
I introduce what is data mining, recommendation system, and Azure. I also show:
1. Participant how to deploy recommendation solution to azure
2. How to use azure storage
3. How to use recommendation solution
Mealionaire: A context-aware and ontology-based mobile recommender system for...Carter Chen
The night markets and the small eats in Taiwan are famous all over the world, eating is the big thing in Taiwan. No matter what time is it, people here in Taiwan can go outdoors, accessing vendors and get something to eat. Although those food vendors and restaurants provide a great variety of fresh food for people to choose, people have a hard time to make decisions sometimes. In this study, we have proposed a mobile recommender system, which is called Mealionaire. The recommender system is context-aware, which means it can perceive the context around the user and the recommendations it produced will adapt to the context whenever the context changes. In addition, it also introduces ontology technologies to analyze the relations between the user, the restaurants and the dishes which are served by the restaurants, in order to provide tailored, personalized results for the user in real-time fashion. The evaluation results show that Mealionaire can provide high user satisfaction, and it is capable to solve the problems that users may face when they are looking for appropriate dishes to eat in their daily lives.
2023 Supervised Learning for Orange3 from scratchFEG
This document provides an overview of supervised learning and decision tree models. It discusses supervised learning techniques for classification and regression. Decision trees are explained as a method that uses conditional statements to classify examples based on their features. The document reviews node splitting criteria like information gain that help determine the most important features. It also discusses evaluating models for overfitting/underfitting and techniques like bagging and boosting in random forests to improve performance. Homework involves building a classification model on a healthcare dataset and reporting the results.
This document provides an overview of unsupervised learning techniques including k-means clustering and association rule mining. It begins with introductions to the speaker and tutorial topics. It then contrasts supervised vs unsupervised learning, describing how k-means is used for clustering without labels and how association rules can discover relationships between items. The document provides examples of applying these techniques in domains like retail, sports, email marketing and healthcare. It also includes visualizations and discusses important concepts for k-means like data transformation and for association rules like support, confidence and lift. Homework questions are asked about preparing data for these algorithms in Orange.
202312 Exploration Data Analysis Visualization (English version)FEG
This document provides an overview of exploratory data analysis (EDA) and visualization techniques that can be performed before building a machine learning model. It introduces the Iris dataset as an example and outlines the key steps of EDA, including loading the data, examining correlations, creating scatter plots, and generating distribution and box plots to understand feature statistics. As homework, students are asked to explore another dataset with a numeric target feature called "housing.tab" and explain the visualizations.
202312 Exploration of Data Analysis VisualizationFEG
This document provides a tutorial on data visualization and analysis using Orange 3. It discusses different types of charts like pie charts, line charts, histograms, bar charts, scatter plots, box plots, and pivot tables. It demonstrates how to visualize survival rates from the Titanic dataset based on features like sex, passenger class, age, and fare paid. Key findings are that women and higher class passengers had higher survival rates, and survival rates also depended on combinations of these features.
Transfer learning (TL) is a research problem in machine learning (ML) that focuses on applying knowledge gained while solving one task to a related task
This document provides a summary of image classification using deep learning techniques. It begins with an introduction to the speaker and their background. It then discusses the main types of image AI tasks like classification, detection, and segmentation. The document reviews the history and timeline of deep learning, important datasets like ImageNet, and algorithms such as convolutional neural networks. It presents the typical process flow for image-based deep learning including feature extraction using convolutional and pooling layers, classification layers, and different network architectures. The document concludes by discussing a homework assignment on building a multi-class image classification model using a dataset of dog, cat, and bird images.
This document provides an introduction and tutorial on using Google Colab. It discusses the speaker's background and experience, then demonstrates how to run sample Python codes in a Colab notebook. It shows how to open an existing Colab file, access computing resources on Colab including GPUs and TPUs, create a new Colab file, and interact with a Google Drive folder to access and save files. The document concludes by providing a homework assignment to have students run Python code in Colab and interact with their Google Drive.
This document provides an introduction and overview of sequence-to-sequence (seq2seq) models, transformer models, attention mechanisms, and BERT for natural language processing. It discusses applications of seq2seq models like language translation and text summarization. Key aspects covered include the encoder-decoder architecture of seq2seq models, how attention improves seq2seq by allowing the model to focus on relevant parts of the context, and the transformer architecture using self-attention rather than recurrent layers. BERT is introduced as a bidirectional transformer model pre-trained on large unlabeled text that achieves state-of-the-art results for a range of NLP tasks. Code examples and homework suggestions are also provided.
This document provides a tutorial on recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. It begins with introductions to the speaker and an overview of the content. It then explains RNNs and how they work sequentially through hidden layers. Issues like vanishing gradients are discussed. LSTMs are introduced as an advanced RNN that can retain information over longer periods of time using gates. Pre-trained word embeddings like Word2Vec, GloVe, and FastText are briefly explained. Finally, homework is assigned to build a sentiment analysis model using an LSTM and pre-trained word embeddings on a Chinese text dataset.
This document provides a tutorial on object detection using YOLOX. It begins with an introduction to the speaker and their background. Next, it discusses popular object detection datasets like PASCAL VOC and MS COCO. It then covers the basics of object detection models including two-stage and one-stage approaches. The document dives into YOLOX specifically, explaining concepts like anchors, Feature Pyramid Network, loss functions, and Non-Maximum Suppression. It includes steps for downloading YOLOX, preparing a dataset using LabelImg, training a model, and testing on images. Homework is assigned to expand the dataset and rebuild the YOLOX model.
This document provides an introduction and tutorial on transfer learning using Keras pretrained models. It discusses using pretrained models like ResNet50 for feature extraction and fine-tuning. The code example shows transferring a pretrained ResNet50 model trained on ImageNet to a CIFAR10 image classification task. Homework involves trying different pretrained models like VGG16, changing trainable layers, or applying the techniques to new tasks.
This document provides a summary of image classification using deep learning. It begins with an introduction to the speaker and their background. It then discusses key concepts in image classification like image types (e.g. raster, vector), feature extraction using convolutional and pooling layers, classification using dense layers and activation functions, and model training. It provides examples of datasets like cats vs dogs and how to balance classes. Finally, it discusses model saving, transformers, and provides homework on modifying the image classification code.
2. About me
• Education
• NCU (MIS)、NCCU (CS)
• Work Experience
• Telecom big data Innovation
• AI projects
• Retail marketing technology
• User Group
• TW Spark User Group
• TW Hadoop User Group
• Taiwan Data Engineer Association Director
• Research
• Big Data/ ML/ AIOT/ AI Columnist
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14. 前饋神經網路
• 一個簡單的單層感知器網路的架構如下
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一個二維 Perceptron 分類器 一個多維(R)輸入的
Perception 分類器
使用 hardlim 當作 activation function,該 function 即是一個 step function,輸入值在給定 threshold 值以上,輸出 1
參考: https://ithelp.ithome.com.tw/articles/10201407
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15. 感知器網路
• 黑點的分類標籤被標註為 1,而白點的分類標
籤被標註為 -1。
• 產生隨機初始化 decision line 的法線向量 w。
• 藍色實線為該步驟所計算得到的 decision line
和它的法線(藍色箭頭)。
• 當 bias 為零, decision line 就必須通過原點。
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參考: https://vocus.cc/article/5cc914d6fd8978000171ac06
16. 感知器網路
• 對所有的訓練樣本,逐一比較 decision line
計算後,更新 decision line 的正交向量。
• 若訓練樣本為黑點,且分類錯誤,則將
decision line 的正交向量往靠近分類錯誤的
訓練樣本的方向移動。在此是利用向量加法
的方式移往黑點所在方向。
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