Rosenlaw & Einschlag Technology Law Offices Lawrence Rosen ●  3001 King Ranch Road, Ukiah, CA 95482 ●  707-485-1242 Michael B. Einschlag ●  25680 Fernhill Drive, Los Altos Hills, CA 94024  ●  650-949-2267 Open Source Licensing Software Freedom and Intellectual Property Law by Lawrence Rosen with a foreword by Lawrence Lessig This book was published by Prentice Hall in July 2004. Please support the

Effective machine learning means building expressive models that sift out signal from noise—that simplify the complexity of real-world data, yet accurately intuit and capture its subtle underlying patterns. Whatever the downstream application, a primary challenge often boils down to this: How do we represent, or even synthesize, complex data in the context of a tractable model? This challenge is c

The main motivation for this post was that I wanted to get more experience with both Variational Autoencoders (VAEs) and with Tensorflow. Thus, implementing the former in the latter sounded like a good idea for learning about both at the same time. This post summarizes the result. Note: The post was updated on December 7th 2015: a bug in the computation of the latent_loss was fixed (removed an err

by Tomas Mikolov, 2010-2012 The Project has been moved to http://rnnlm.org Introduction Neural network based language models are nowdays among the most successful techniques for statistical language modeling. They can be easily applied in wide range of tasks, including automatic speech recognition and machine translation, and provide significant improvements over classic backoff n-gram models. The

Architects look at thousands of buildings during their training, and study critiques of those buildings written by masters. In contrast, most software developers only ever get to know a handful of large programs well—usually programs they wrote themselves—and never study the great programs of history. As a result, they repeat one another's mistakes rather than building on one another's successes.

Nグラムは一般に公開されている日本語のWebページでGoogleがクロールしたものから抽出されている。ただし、閲覧に特別な認証が必要なページや、metaタグにnoarchive,noindex 等が指定されているページは対象に入っていない。抽出対象となった文数は約200億文で、出現頻度20回以上の１～７グラムを収録している。 データの詳細については付属のREADMEも参照のこと。