The document discusses Python programming and data science tools like NumPy, Scikit-learn, and Cython. It provides examples of using NumPy to quickly sum a large array and speed up a prime number calculation with Cython. It also briefly mentions past Python conference talks and techniques like spectral clustering and activation functions.
Fast and Probvably Seedings for k-MeansKimikazu Kato
?
The document proposes a new MCMC-based algorithm for initializing centroids in k-means clustering that does not assume a specific distribution of the input data, unlike previous work. It uses rejection sampling to emulate the distribution and select initial centroids that are widely scattered. The algorithm is proven mathematically to converge. Experimental results on synthetic and real-world datasets show it performs well with a good trade-off of accuracy and speed compared to existing techniques.
This document discusses Python and machine learning libraries like scikit-learn. It provides code examples for loading data, fitting models, and making predictions using scikit-learn algorithms. It also covers working with NumPy arrays and loading data from files like CSVs.
Introduction to behavior based recommendation systemKimikazu Kato
?
Material presented at Tokyo Web Mining Meetup, March 26, 2016.
The source code is here:
https://github.com/hamukazu/tokyo.webmining.2016-03-26
東京ウェブマイニング(2016年3月27)の発表資料です。すべて英語です。
Recommendation System --Theory and PracticeKimikazu Kato
?
This document provides an overview of recommendation systems and collaborative filtering techniques. It discusses using matrix factorization to predict user ratings by representing users and items as vectors in a latent factor space. Optimization techniques like stochastic gradient descent can be used to learn the factorization from existing ratings. The document also notes challenges of sparsity and scale for practical systems and describes approaches like elastic net regularization and sparsification to address these.
Effective Numerical Computation in NumPy and SciPyKimikazu Kato
?
This document provides an overview of effective numerical computation in NumPy and SciPy. It discusses how Python can be used for numerical computation tasks like differential equations, simulations, and machine learning. While Python is initially slower than languages like C, libraries like NumPy and SciPy allow Python code to achieve sufficient speed through techniques like broadcasting, indexing, and using sparse matrix representations. The document provides examples of how to efficiently perform tasks like applying functions element-wise to sparse matrices and calculating norms. It also presents a case study for efficiently computing a formula that appears in a machine learning paper using different sparse matrix representations in SciPy.
Kimikazu Kato is the Chief Scientist at Silver Egg Technology, which provides recommender system and online advertising services. He has a PhD in computer science and experience in areas like computer graphics and parallel computing. Silver Egg uses a real-time recommender platform called Aigent Suite to consistently target users from initial visits to retention. The system analyzes user behavior data to determine personalized recommendations and ad targeting. While collaborative filtering and matrix factorization are common recommendation algorithms, approaches need adjustments for sales recommendations versus movie ratings. Consulting is also important for tuning algorithm parameters to specific business needs.
IoT Devices Compliant with JC-STAR Using Linux as a Container OSTomohiro Saneyoshi
?
Security requirements for IoT devices are becoming more defined, as seen with the EU Cyber Resilience Act and Japan’s JC-STAR.
It's common for IoT devices to run Linux as their operating system. However, adopting general-purpose Linux distributions like Ubuntu or Debian, or Yocto-based Linux, presents certain difficulties. This article outlines those difficulties.
It also, it highlights the security benefits of using a Linux-based container OS and explains how to adopt it with JC-STAR, using the "Armadillo Base OS" as an example.
Feb.25.2025@JAWS-UG IoT
25. 記法
? 既存関数を遅延評価するときは頭に$を付
ける
? 遅延評価する関数を新たに定義するとき
は、関数名の前にlazyを付ける
? 強制的に評価するときには、force関数を
適用 例:n番目の素数を返す関数primes n
val p=$primes 1000000 一瞬で終わる(計算しない)
val q=force p 時間がかかる
val r=force p 一瞬で終わる(答を覚えている)