An interactive visualization of the Operational Transformation integration algorithm with a central server
NumPyro Release We’re excited to announce the release of NumPyro, a NumPy-backed Pyro using JAX for automatic differentiation and JIT compilation, with over 100x speedup for HMC and NUTS! See the examples and documentation for more details. Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend. Pyro enables flexible and expressive de
MLU-EXPLAIN Visual explanations of core machine learning concepts Machine Learning University (MLU) is an education initiative from Amazon designed to teach machine learning theory and practical application. As part of that goal, MLU-Explain exists to teach important machine learning concepts through visual essays in a fun, informative, and accessible manner. Neural Networks Learn about neural net
This article introduces readers to the mean-variance optimization of asset portfolios. The underlying formulas are implemented in Python. Market data has been downloaded from Google Finance. The case study is available here. Calculation of assets' weights, returns and covariances Our example starts with the calculation of vector of asset weights \(\mathbf{W}_{n \times 1}\), expected annual returns
In programming languages, a delimited continuation, composable continuation or partial continuation, is a "slice" of a continuation frame that has been reified into a function. Unlike regular continuations, delimited continuations return a value, and thus may be reused and composed. Control delimiters, the basis of delimited continuations, were introduced by Matthias Felleisen in 1988[1] though ea
Understanding Convolutions on Graphs Ameya Daigavane, Balaraman Ravindran, and Gaurav Aggarwal Understanding the building blocks and design choices of graph neural networks. A Gentle Introduction to Graph Neural Networks Benjamin Sanchez-Lengeling, Emily Reif, Adam Pearce, and Alexander B. Wiltschko What components are needed for building learning algorithms that leverage the structure and propert
Apache TVM is a machine learning compilation framework, following the principle of Python-first development and universal deployment. It takes in pre-trained machine learning models, compiles and generates deployable modules that can be embedded and run everywhere. About Apache TVM The vision of the Apache TVM Project is to host a diverse community of experts and practitioners in machine learning,
この記事はTensorFlow Advent Calendar 2020の6日目の記事です。 概要 この記事では NumPyro について扱います。NumPyro は確率的プログラミングを行うためのフレームワークの1つで、バックエンドに JAX を使っていることが特徴的です。この時点で次のような疑問が生まれるでしょう。 そもそもなんで確率なの？サイコロを投げるの？ 確率的プログラミングとは？ JAXって？ これにできる限り真正面から答えようというのがこの記事の目的です。まず確率モデルを導入する理由について述べます。次に、確率的プログラミングが扱う課題について述べます。その後、 NumPyro に関係する技術である Pyro や JAX について確認したあとに NumPyro について触れます。 このような構成のため、それぞれの構成要素について深くは触れません。また、ベイズ推論や機械学習に関
社会学系の研究会からの依頼で、(1)因果推論の諸理論が奥の方でどう繋がっているか、また(2)そこで「質的理解」と「量的分析」がどう繋がっているのか、についてお話したときの資料です／ 訂正情報 (1) 誤：Neiman→正：Neyman, (2) スライド97枚目 誤:p(Y|X=小さい, Z) = p(Y|X=do(小さい))→正：E_{Z}[p(Y|X=小さい, Z)] = p(Y|X=do(小さい))（＊SlideShareではスライドの再アップロードが不可のためスライド自体を直せないのです...）
Introducing KickStart — AI generated FormKit forms in seconds. Generate from a screenshot, edit with drag-and-drop or conversational AI, copy & paste as components or schema! Try for free Add motion to your apps with a single line of code. AutoAnimate is a zero-config, drop-in animation utility that adds smooth transitions to your web app. You can use it with React, Solid, Vue, Svelte, or any oth
Co-authors: Xiang Zhang and Jingyu Zhu Introduction The Lambda architecture has become a popular architectural style that promises both speed and accuracy in data processing by using a hybrid approach of both batch processing and stream processing methods. But it also has some drawbacks, such as complexity and additional development/operational overheads. One of our features for Premium members on
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