This document discusses representing data as vectors. It explains that vectors are simply sets of numbers, and gives examples of representing human body measurements and image pixels as vectors of various dimensions. Higher-dimensional vectors can be used to encode complex data like images, time series, and survey responses. Visualizing vectors in coordinate systems becomes more abstract in higher dimensions. The key point is that vectors provide a unified way to represent diverse types of data.
The document discusses various measures of central tendency, including the mean, median, and weighted mean. It explains how to calculate the arithmetic mean of a data set by summing all values and dividing by the number of values. The mean works for vector data by taking the mean of each dimension separately. However, the mean can be overly influenced by outliers. Alternatively, the median is not influenced by outliers, as it is the middle value of the data when sorted. The document provides examples to illustrate these concepts for measuring central tendency.
This document discusses representing data as vectors. It explains that vectors are simply sets of numbers, and gives examples of representing human body measurements and image pixels as vectors of various dimensions. Higher-dimensional vectors can be used to encode complex data like images, time series, and survey responses. Visualizing vectors in coordinate systems becomes more abstract in higher dimensions. The key point is that vectors provide a unified way to represent diverse types of data.
The document discusses various measures of central tendency, including the mean, median, and weighted mean. It explains how to calculate the arithmetic mean of a data set by summing all values and dividing by the number of values. The mean works for vector data by taking the mean of each dimension separately. However, the mean can be overly influenced by outliers. Alternatively, the median is not influenced by outliers, as it is the middle value of the data when sorted. The document provides examples to illustrate these concepts for measuring central tendency.
The document discusses distances between data and similarity measures in data analysis. It introduces the concept of distance between data as a quantitative measure of how different two data points are, with smaller distances indicating greater similarity. Distances are useful for tasks like clustering data, detecting anomalies, data recognition, and measuring approximation errors. The most common distance measure, Euclidean distance, is explained for vectors of any dimension using the concept of norm from geometry. Caution is advised when calculating distances between data with differing scales.
1. The document discusses principal component analysis (PCA) and explains how it can be used to determine the "true dimension" of vector data distributions.
2. PCA works by finding orthogonal bases (principal components) that best describe the variance in high-dimensional data, with the first principal component accounting for as much variance as possible.
3. The lengths of the principal component vectors indicate their importance, with longer vectors corresponding to more variance in the data. Analyzing the variances of the principal components can provide insight into the shape of the distribution and its true dimension.
The document discusses pattern recognition and classification. It begins by defining pattern recognition as a method for determining what something is based on data like images, audio, or text. It then provides examples of common types of pattern recognition like image recognition and speech recognition. It notes that while pattern recognition comes easily to humans, it can be difficult for computers which lack abilities like unconscious, high-speed, high-accuracy recognition. The document then discusses the basic principle of computer-based pattern recognition as classifying inputs into predefined classes based on their similarity to training examples.
This document discusses using linear algebra concepts to analyze data. It explains that vectors can be used to represent data, with each component of the vector corresponding to a different attribute or variable. The amount of each attribute in the data is equivalent to the component value. Vectors can be decomposed into the sum of their components multiplied by basis vectors, and recomposed using those values. This relationship allows the amount of each attribute to be calculated using the inner product of the vector and basis vector. So linear algebra provides a powerful framework for understanding and analyzing complex, multi-dimensional data.
This document discusses non-structured data analysis, focusing on image data. It defines structured and non-structured data, with images, text, and audio given as examples of non-structured data. Images are described as high-dimensional vectors that are generated from analog to digital conversion via sampling and quantization. Various types of image data and analysis tasks are introduced, including image recognition, computer vision, feature extraction and image compression. Image processing techniques like filtering and binarization are also briefly covered.
The document discusses distances between data and similarity measures in data analysis. It introduces the concept of distance between data as a quantitative measure of how different two data points are, with smaller distances indicating greater similarity. Distances are useful for tasks like clustering data, detecting anomalies, data recognition, and measuring approximation errors. The most common distance measure, Euclidean distance, is explained for vectors of any dimension using the concept of norm from geometry. Caution is advised when calculating distances between data with differing scales.
1. The document discusses principal component analysis (PCA) and explains how it can be used to determine the "true dimension" of vector data distributions.
2. PCA works by finding orthogonal bases (principal components) that best describe the variance in high-dimensional data, with the first principal component accounting for as much variance as possible.
3. The lengths of the principal component vectors indicate their importance, with longer vectors corresponding to more variance in the data. Analyzing the variances of the principal components can provide insight into the shape of the distribution and its true dimension.
The document discusses pattern recognition and classification. It begins by defining pattern recognition as a method for determining what something is based on data like images, audio, or text. It then provides examples of common types of pattern recognition like image recognition and speech recognition. It notes that while pattern recognition comes easily to humans, it can be difficult for computers which lack abilities like unconscious, high-speed, high-accuracy recognition. The document then discusses the basic principle of computer-based pattern recognition as classifying inputs into predefined classes based on their similarity to training examples.
This document discusses using linear algebra concepts to analyze data. It explains that vectors can be used to represent data, with each component of the vector corresponding to a different attribute or variable. The amount of each attribute in the data is equivalent to the component value. Vectors can be decomposed into the sum of their components multiplied by basis vectors, and recomposed using those values. This relationship allows the amount of each attribute to be calculated using the inner product of the vector and basis vector. So linear algebra provides a powerful framework for understanding and analyzing complex, multi-dimensional data.
This document discusses non-structured data analysis, focusing on image data. It defines structured and non-structured data, with images, text, and audio given as examples of non-structured data. Images are described as high-dimensional vectors that are generated from analog to digital conversion via sampling and quantization. Various types of image data and analysis tasks are introduced, including image recognition, computer vision, feature extraction and image compression. Image processing techniques like filtering and binarization are also briefly covered.
20151104 health2.0 in Japan - clinical big data and subjectsNaotoKume
Japanese slides:
Current state of EHR development in Japan. Nation-wide EHR.
医療現場にあるビッグデータとその課題
Health 2.0 Day1
https://health2.medpeer.co.jp/program.html#A04
Recursively Summarizing Books with Human Feedbackharmonylab
公開URL:https://arxiv.org/abs/2109.10862
出典:Jeff Wu, Long Ouyang, Daniel M. Ziegler, Nisan Stiennon, Ryan Lowe, Jan Leike, Paul Christiano : Recursively Summarizing Books with Human Feedback, arXiv:2109.10862 (2021).
概要:MLモデルの学習のために行動の良し悪しを表すtraining signalを人間がループの中で提供する必要があるタスクが多く存在する.人間による評価に時間や専門的な知識を要するタスクの学習のためには,効果的なtraining signalを生成するためのスケーラブルな手法が必要となる.本論文では書籍全体の要約タスク(abstractive)を対象として,再帰的なタスクの分解と人間のフィードバックからの学習を組み合わせたアプローチを紹介する.モデルによる要約の中には人間が書いた要約の品質に匹敵する要約もあるが,平均するとモデルの要約は人間の要約に著しく劣ることが示された.
This document introduces artificial intelligence (AI) and discusses examples of AI being used in everyday life. It defines AI as machines that mimic human intelligence, and notes most current AI is specialized or "weak AI" that can only perform specific tasks rather than general human-level intelligence. Examples discussed include voice recognition, chatbots, facial recognition, image recognition for medical diagnosis, recommendation systems, AI in games like Go, and applications in business like sharing economies and customer monitoring.
This document provides an introduction to probability and probability distributions for data analysis. It explains that probability, like histograms, can help understand how data is distributed. Probability distributions describe the "easiness" or likelihood that a random variable takes on a particular value, and can be discrete (for a finite number of possible values) or continuous (for infinite possible values). Key probability distributions like the normal distribution are fundamental to many statistical analyses and machine learning techniques. Understanding probability distributions allows expressing data distributions with mathematical formulas parameterized by a few values.
The document discusses predictive modeling and regression analysis using data. It explains that predictive modeling involves collecting data, creating a predictive model by fitting the model to the data, and then using the model to predict outcomes for new input data. Regression analysis specifically aims to model relationships between input and output variables in data to enable predicting outputs for new inputs. The document provides examples of using linear regression to predict exam scores from study hours, and explains that the goal in model fitting is to minimize the sum of squared errors between predicted and actual output values in the data.
This document discusses clustering and anomaly detection in data science. It introduces the concept of clustering, which is grouping a set of data into clusters so that data within each cluster are more similar to each other than data in other clusters. The k-means clustering algorithm is described in detail, which works by iteratively assigning data to the closest cluster centroid and updating the centroids. Other clustering algorithms like k-medoids and hierarchical clustering are also briefly mentioned. The document then discusses how anomaly detection, which identifies outliers in data that differ from expected patterns, can be performed based on measuring distances between data points. Examples applications of anomaly detection are provided.
Machine learning for document analysis and understandingSeiichi Uchida
The document discusses machine learning and document analysis using neural networks. It begins with an overview of the nearest neighbor method and how neural networks perform similarity-based classification and feature extraction. It then explains how neural networks work by calculating inner products between input and weight vectors. The document outlines how repeating these feature extraction layers allows the network to learn more complex patterns and separate classes. It provides examples of convolutional neural networks for tasks like document image analysis and discusses techniques for training networks and visualizing their representations.
An opening talk at ICDAR2017 Future Workshop - Beyond 100%Seiichi Uchida
What are the possible future research directions for OCR researchers (when we achieve almost 100% accuracy)? This slide is for a short opening talk to stimulate audiences. Actually, young researchers on OCR or other document processing-related research need to think about their "NEXT".