機械学習の社会実装では、予測精度が高くても、機械学習がブラックボックであるために使うことができないということがよく起きます。
このスライドでは機械学習が不得意な予測結果の根拠を示すために考案されたLIMEの論文を解説します。
Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. "" Why should i trust you?" Explaining the predictions of any classifier." Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 2016.
データマイニングや機械学習をやるときによく問題となる「リーケージ」を防ぐ方法について論じた論文「Leakage in Data Mining: Formulation, Detecting, and Avoidance」(Kaufman, Shachar, et al., ACM Transactions on Knowledge Discovery from Data (TKDD) 6.4 (2012): 1-21.)を解説します。
主な内容は以下のとおりです。
・過去に起きたリーケージの事例の紹介
・リーケージを防ぐための2つの考え方
・リーケージの発見
・リーケージの修正
[DL輪読会]Generative Models of Visually Grounded ImaginationDeep Learning JP
The document proposes a new model for visually grounded semantic imagination that can generate images from linguistic descriptions of concepts specified by attributes. The model uses a variational autoencoder with three inference networks to handle images, attributes, and missing modalities. It represents the attribute inference distribution as the product of expert Gaussians, allowing generation of concepts not seen during training by combining learned attributes. The paper introduces three criteria for evaluating such models: correctness, coverage, and compositionality.
This document discusses Mahout, an Apache project for machine learning algorithms like classification, clustering, and pattern mining. It describes using Mahout with Hadoop to build a Naive Bayes classifier on Wikipedia data to classify articles into categories like "game" and "sports". The process includes splitting Wikipedia XML, training the classifier on Hadoop, and testing it to generate a confusion matrix. Mahout can also integrate with other systems like HBase for real-time classification.
Introducton to Convolutional Nerural Network with TensorFlowEtsuji Nakai
Explaining basic mechanism of the Convolutional Neural Network with sample TesnsorFlow codes.
Sample codes: https://github.com/enakai00/cnn_introduction
Machine Learning Basics for Web Application DevelopersEtsuji Nakai
This document provides an overview of machine learning basics for web application developers. It discusses linear binary classifiers and logistic regression, how to measure model fitness with loss functions, and graphical understandings of linear classifiers. It then covers linear multiclass classifiers using softmax functions, image classification with neural networks, and ways to improve accuracy using convolutional neural networks. Finally, it discusses client applications that use pre-trained machine learning models through API services and examples of smile detection and cucumber classification.
Your first TensorFlow programming with JupyterEtsuji Nakai
This document provides an introduction and overview of TensorFlow and how to use it with Jupyter notebooks on Google Cloud Platform (GCP). It explains that TensorFlow is Google's open source library for machine learning and was launched in 2015. It is used for many production machine learning projects. Jupyter is introduced as an interactive web-based platform for data analysis that can also be used as a TensorFlow runtime environment. The document then provides details on the programming paradigm and model of TensorFlow, giving an example of using it for a least squares method problem to predict temperatures. It explains the key components of defining a model, loss function, and training algorithm to optimize variables in a session.
This document provides an introduction to deep Q-networks (DQN) for beginners. It explains that DQNs can be used to learn optimal actions in video games by collecting data on screen states, player actions, rewards, and next states without knowing the game's rules. The key idea is to approximate a "Q function" that represents the total expected rewards if optimal actions are taken from each state onward. A deep neural network is used as the candidate function, and its parameters are adjusted using an error function to satisfy the Q-learning equation. To collect the necessary state-action data, the game is played with a mix of random exploration and exploiting the current best actions from the Q-network.
29. 29
CodeZine Academy
オーバーフィッティングを意図的に抑える手法
- M = 9 の高次の項は絶対値が突出し
て大きくなっています。これは、
パラメータの過剰調整であり、
オーバーフィッティングの兆候と
考えられます。
Table of the coefficients
M=0 M=1 M=3 M=9
0 -0.02844 0.498661 -0.575134 -0.528572
1 NaN -1.054202 12.210765 151.946893
2 NaN NaN -29.944028 -3569.939743
3 NaN NaN 17.917824 34234.907567
4 NaN NaN NaN -169228.812728
5 NaN NaN NaN 478363.615824
6 NaN NaN NaN -804309.985246
7 NaN NaN NaN 795239.975974
8 NaN NaN NaN -426702.757987
9 NaN NaN NaN 95821.189286
■
N = 10 の例で実際に計算された係数 の値を見ると下表のようにな
ります。
■
そこで、適当な定数 λ を用いて、下記のように修正した誤差関数を最小にする
という条件で係数を決めると、次数が高くでもオーバーフィッティングが発生
しにくくなります。
- 最適な λ の値は、試行錯誤で決める必要があります。