NagoyaStat #12 で使用した資料です(公開に当たって当日ホワイトボードに書いた内容等を補完したものになります)。
「StanとRでベイズ統計モデリング」の第9章前半になります。
第9章のテーマは行列やベクトルを使った演算の高速化です。
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The title of textbook is "Bayesian statistical modeling with Stan and R", and that of Chapter 9 in textbook is "advanced grammar" in English.
This presentation slide explains the procedure of creating a data set in the IBM SPSS statistics and also explains the way to import the excel data set into the SPSS.
This document contains code for building interactive Shiny apps with Leaflet maps. It defines user input controls to select a geographic region and location. Map layers are added and updated based on the user selections. Code is also included to zoom the map to the selected location bounds and identify the clicked feature.
The document discusses the paper "t-vMF Similarity for Regularizing Intra-Class Feature Distribution" presented at CVPR2021. The paper proposes a new similarity measure called t-vMF similarity that can control the width of the peak and skirt of the cosine similarity. This allows intra-class variance to be reduced while preventing gradient vanishing, especially for imbalanced or small-scale datasets where maximizing discrimination is more important than minimizing intra-class variance. The t-vMF similarity is implemented by considering the von Mises-Fisher distribution in the process of the softmax cross-entropy loss, making it simple to implement.
This document discusses data visualization tools in Python. It introduces Matplotlib as the first and still standard Python visualization tool. It also covers Seaborn which builds on Matplotlib, Bokeh for interactive visualizations, HoloViews as a higher-level wrapper for Bokeh, and Datashader for big data visualization. Additional tools discussed include Folium for maps, and yt for volumetric data visualization. The document concludes that Python is well-suited for data science and visualization with many options available.
This tutorial by Simplilearn will explain to you What Is File Handling In C? File Handling In C Programming tutorial will help you learn the operations of file handling, the functions of file handling in c, and file opening modes in c. This C programming tutorial will cover both theoretical and practical demonstrations for a better learning experience on File Handling In C programming.
This document analyzes Japanese sake brand data to cluster brands and identify similarities. It first calculates cosine distances between brands based on flavor profiles. It then performs hierarchical clustering on the distances to group brands into 6 clusters. Finally, it creates radar charts of average flavor profiles and word clouds of common tags for each cluster to visualize differences between the groups.
The document discusses the paper "t-vMF Similarity for Regularizing Intra-Class Feature Distribution" presented at CVPR2021. The paper proposes a new similarity measure called t-vMF similarity that can control the width of the peak and skirt of the cosine similarity. This allows intra-class variance to be reduced while preventing gradient vanishing, especially for imbalanced or small-scale datasets where maximizing discrimination is more important than minimizing intra-class variance. The t-vMF similarity is implemented by considering the von Mises-Fisher distribution in the process of the softmax cross-entropy loss, making it simple to implement.
This document discusses data visualization tools in Python. It introduces Matplotlib as the first and still standard Python visualization tool. It also covers Seaborn which builds on Matplotlib, Bokeh for interactive visualizations, HoloViews as a higher-level wrapper for Bokeh, and Datashader for big data visualization. Additional tools discussed include Folium for maps, and yt for volumetric data visualization. The document concludes that Python is well-suited for data science and visualization with many options available.
This tutorial by Simplilearn will explain to you What Is File Handling In C? File Handling In C Programming tutorial will help you learn the operations of file handling, the functions of file handling in c, and file opening modes in c. This C programming tutorial will cover both theoretical and practical demonstrations for a better learning experience on File Handling In C programming.
This document analyzes Japanese sake brand data to cluster brands and identify similarities. It first calculates cosine distances between brands based on flavor profiles. It then performs hierarchical clustering on the distances to group brands into 6 clusters. Finally, it creates radar charts of average flavor profiles and word clouds of common tags for each cluster to visualize differences between the groups.