本スライドは、弊社の鈴木により2021年3月25日のArithmer Seminarで使用されたものです。
弊社のNLPソリューションの基礎的な部分について、営業メンバー・非DBエンジニアに理解してもらう目的で作った資料になります。
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
Raspberry Pi ではじめる機械学習(https://amzn.to/2VbGrFH)の数字認識についてまとめてます.
興味のある人はやってみてください.
詳細ブログ:https://kenyu-life.com/2018/11/06/raspberry_pi_machin_learning_numbers/
本スライドは、弊社の鈴木により2021年3月25日のArithmer Seminarで使用されたものです。
弊社のNLPソリューションの基礎的な部分について、営業メンバー・非DBエンジニアに理解してもらう目的で作った資料になります。
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
Raspberry Pi ではじめる機械学習(https://amzn.to/2VbGrFH)の数字認識についてまとめてます.
興味のある人はやってみてください.
詳細ブログ:https://kenyu-life.com/2018/11/06/raspberry_pi_machin_learning_numbers/
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.
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.
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.
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。