The ppt is about how to use Refine search improve longtailed product sale and user experience. It is writed in Chinese, if you need English version, please contact us directly.
The ppt is about how to use Refine search improve longtailed product sale and user experience. It is writed in Chinese, if you need English version, please contact us directly.
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.