Feed: Featured Blog Posts - Data Science Central. Author: Andrea Manero-Bastin. This article was written by Louis Tiao.   In this series of notebooks, we demonstrate some useful patterns and recipes for visualizing animating optimization algorithms using Matplotlib.     We shall restrict our attention to 3-dimensional problems for right now (i.e. optimizing over only 2 parameters), though what follows can be extended to higher dimensions by plotting all pairs of parameters against each other, effectively projecting the problem to 3-dimensions. The Wikipedia article on Test functions for optimization has a few functions that are useful for evaluating optimization algorithms. In particular, ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Richard Charles, PhD. One of my most memorable experiences during my college years was having an English professor who spoke entirely in complete sentences.There were no spoken sentence fragments, dangling thoughts or misuse of adjectives, verbs or nouns. When I first met him, I was stunned at his level of commitment to this principle. I often wondered how challenging this must be for him and whether or not the journey had instances where a thought started and had to be gracefully abandoned into a mundane compromise of a sentence. It was ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Bill Schmarzo.  Before I kick off this new blog, I’m happy to announce the release of my 3rd book” "The Art of Thinking Like A Data Scientist”. This book is designed to be a workbook – a pragmatic tool that you can use to help your organization leverage data and analytics to power your business and operational models. The book is jammed with templates, worksheets, examples and hands-on exercises, all composed to help reinforce and deploy the fundamental concepts of Thinking Like A Data Scientist.  I hope you enjoy it! The ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Ayumi Owada. Ecommerce sites generate tons of web server log data which can provide valuable insights through analysis. For example, if we know which users are more likely to buy a product, we can perform targeted marketing, improve relevant product placement on our site and lift conversion rates. However, raw web logs are often enormous and messy so preparing the data to train a predictive model is time consuming for data scientists.According to a recent Forbes article, “Data preparation accounts for about 80% of the work of data scientists”, but “76% ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Andrea Manero-Bastin. This article was written by ODSC - Open Data Science in Predict.   Machine learning is one of the most exciting new features used in technology today. However, it is undoubtedly controversial too. At the moment, this controversy does not really come from the prospect of ultra-intelligent robots dramatically taking over the human race; instead, it’s because powerful technology like this can be used just as negatively by criminals, as it can be used positively by those with good intention. In this piece, I would like to explore the ... Read More

Feed: SAS Blogs. Author: Léonie Valencia. Die Anwendungsbereiche des Internet of Things (IoT) sind äußerst vielseitig. Ob in Verbindung mit den Themen Industrie 4.0, Artificial Intelligence, Predictive Maintenance, Machine Learning oder auch Streaming und Edge Analytics – es gibt undenkbar viele Möglichkeiten. Besonders im produzierenden Bereich ermöglichen neue Technologien umfassende Anwendungen zur Effizienzsteigerung und Prozessoptimierung. Doch so gut das auch klingen mag, es gehen damit natürlich auch Herausforderungen einher, die gerade für Unternehmen ohne etablierte IT-Architekturen eine disruptive Veränderung und viel Vorarbeit bedeuten. Immer wieder fällt in diesem Zusammenhang auf, wie wichtig die interdisziplinäre Kooperation zwischen Hardware-, Software- und Infrastrukturanbietern ... Read More

Feed: R-bloggers. Author: Kristian Larsen. Category Tags Regression analysis is one of the most demanding machine learning methods in 2019. One group of regression analysis for measuring effects and to evaluate a policy program is Difference-in-Difference. This method is well suited for benchmarking and finding improvements for optimization in organizations. It can, therefore, be used to design organizations so they generate more value for employees and customers. In this article, you learn how to do difference-in-difference in R. Methodology The difference in differences (DiD) method is a statistical technique or quasi-experimental design method, and it is used primarily in the ... Read More

Feed: R-bloggers. Author: R by R(yo). The useR! Conference was held in Toulouse, France and for me thiswas my second useR! after my first in Brisbane last year. This timearound I wanted to write about my experiences and some highlightssimilar to my post on the RStudio::Conference 2019 & Tidyverse DevDayearlier this year. This blog post will be divided into 4 sections: Programming, Shiny, {Packages}, and Touring Toulouse. You can find slides and videos (in a week or so) in: As usual there were many talks that I didn’t get to go to as there arearound 3~5 tracks across different rooms ... Read More

Feed: Planet MySQL; Author: Krunal Lathiya; SQL Aliases Example | Alias In SQL Tutorial is today’s topic. Aliases are the temporary names that exist for a particular query. It is used for better understanding and reducing the workload of writing big column names. Aliases are the temporary name and the changes made with the column name while retrieving does not reflect in the original database. Content Overview SQL Aliases Example An Alias is a shorthand for the table or column name. Aliases reduce the amount of typing required to write the query. Complex queries with the aliases are generally easier ... Read More

Feed: SAS Blogs. Author: Makoto Unemi (畝見 真). SAS Viyaのディープラーニング機能をPythonから利用するためのハイレベルAPIパッケージの最新版であるDLPy1.1では、主にCNN(Convolutional Neural Network)に関連する機能が拡張されています。 主な拡張機能： ・新たに３つのネットワーク構造に対応 【U-Net】元々は、医療用画像のセグメンテーション向けに開発されたネットワークです。（出典：https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/） 【MobileNet】モバイル端末のようなリソースの少ない環境でも、畳み込み計算を分割（Depthwise Separable Convolution）することで、軽快に、素早く、そして精度の高い結果を得ることができると言われているネットワークです。 左が一般的な畳み込み構造。右が、MobileNetの構造。（出典：https://arxiv.org/pdf/1704.04861.pdf） 【ShuffleNet】MobileNet同様に軽量軽快なネットワークですが、MobileNetでの畳み込みの分割に加えて、その名の通り、チャンネルをシャッフルしてチャンネル間での畳み込みを行い、特徴抽出を効率化するネットワーク構造です。 （出典：https://arxiv.org/pdf/1707.01083.pdf） ・上記ネットワーク構造に伴う、畳み込み層機能の拡張 【transpose convolution（転置畳み込み）】Deconvolution（逆畳み込み）とも言われ、元となる画像に0 paddingして拡大してから畳み込む手法です。（上記U-Netに関連） 【group convolution（グループ化畳み込み）】入力層をチャンネル方向にグループ分割して、グループごとに畳み込みを行い、最後に結合して出力する手法です。分割することで計算量を小さくすることができます。（上記MobileNet、ShuffleNetに関連） ・画像解析手法の拡張 【物体検出（Object Detection）手法にFaster R-CNNを追加】R-CNNからFast R-CNN、そしてFaster R-CNNへと処理時間の短縮と精度向上が図られ進化してきているアルゴリズムです。（出典：https://arxiv.org/pdf/1506.01497.pdf） DLPyの従来版からサポートしているYOLOに比べると処理時間はかかりますが、より高い精度を得ることができます。 【新たにセグメンテーション（Semantic Segmentation）に対応】セグメンテーションは、画像中に存在する複数の物体や領域に対して、ピクセルレベルで推定する問題です。画像を入力すると、各画素に対して識別結果が付与された画像を出力します。一般的には、 Nクラスのセグメンテーションモデルは、Nチャンネルの出力画像を出力し、各チャンネルの画素値は各クラスの確率を表します。（上記U-Netに関連） 以上のように、PythonユーザーがDLPyを通して活用することができる、SAS Viyaのディープラーニング（CNN）機能が拡張されています。 ※DLPyの詳細に関しては、Githubサイトでご覧いただけます。※Enterprise Open Analytics Platform 「SAS Viya」 を知りたいなら「特設サイト」へGO！ ... Read More