The Little Book of Deep Learning
The Little Book of Deep Learning François Fleuret François Fleuret is a professor of computer sci- ence at the University of Geneva, Switzerland. The cover illustration is a schematic of the Neocognitron by Fukushima [1980], a key an- cestor of deep neural networks. This ebook is formatted to fit on a phone screen. Contents Contents 5 List of figures 7 Foreword 8 I Foundations 10 1 Machine Learnin
Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors. I continue my AI poetry generation experiments with OpenAI’s GPT-3 (released mid-2020), which is 116× larger, and much more powerful, than the 2019 GPT-2. GPT-3, however, is not merely a quantitative tweak y
Author’s Note: Thanks, as ever, to Fred Hebert, for reading a draft of this post and making some sterling suggestions. This is the third installment in my series on testing distributed systems. The posts in this series are the following: Testing Microservices, the sane way (published December 2017) Testing in Production, the safe way (published March 2018) Testing in Production: the hard parts (pu
互いに独立でなくてもできる中心極限定理と, そのデモ (Gordin's CLT/Donsker定理) - ill-identified diary
概要 はじめに シミュレーション IIDな時系列 (基本) 独立ではないケース1: AR(1) 2022/1/17 追記: マルチンゲール差分列の中心極限定理 独立ではないケース2: ランダムウォーク 統計学への応用 相関ありの中心極限定理の応用 汎関数中心極限定理の応用 参考文献 概要今月まだ何も書いてなかったのでタイトルの通り中心極限定理の発展的な話をする. といってもAR(1)とランダムウォーク乱数のグラフを描いただけなんだけど. 対象読者: 統計学の入門的な教科書に書いてある中心極限定理 (CLT) や大数の法則は知っているが, そこから先は知らない人 はじめにほとんどの基礎的な教科書に書いてある回帰分析や機械学習のモデルではデータが互いに独立かつ同一の分布 (IID) であると仮定している. これは大数の法則や中心極限定理が成り立つ条件の1つでもあり, よって十分にデータが多けれ
The Decision Tree algorithm is a type of tree-based modeling under Supervised Machine Learning. Decision Trees are primarily used to solve classification problems (the algorithm, in this case, is called the Classification Tree), but they can also be used to solve regression problems (the algorithm, in this case, is called the Regression Tree). The concept of trees is found in graph theory and is u
A Bayesian network is a graphical model representing probabilistic relationships among variables. Introduction Probabilistic models are based on the theory of probability. I guess that was quite self-explanatory, considering it is in the name. Probabilistic models consider the fact that randomness plays a role in predicting future outcomes. The opposite of randomness is deterministic, which tells
Jimenez is a Professor of Chemistry and a Fellow of the Cooperative Institute for Research in Environmental Sciences at the University of Colorado-Boulder. He is a highly cited researcher and a Fellow of the American Association for Aerosol Research and the American Geophysical Union. Many months into the COVID-19 pandemic, the coronavirus is still spreading uncontrolled through the U.S. Public he
Patterns.PDF
Robert C. Martin Copyright (c) 2000 by Robert C. Martin. All Rights Reserved. www.objectmentor.com 1 Design Principles and Design Patterns Robert C. Martin www.objectmentor.com What is software architecture? The answer is multitiered. At the highest level, there are the architecture patterns that define the overall shape and structure of software applications1 . Down a level is the architecture th
Introduction to Linear Algebra for Applied Machine Learning with Python
Introduction to Linear Algebra for Applied Machine Learning with Python Linear algebra is to machine learning as flour to bakery: every machine learning model is based in linear algebra, as every cake is based in flour. It is not the only ingredient, of course. Machine learning models need vector calculus, probability, and optimization, as cakes need sugar, eggs, and butter. Applied machine learni
Eight Things to Know about Large Language Models Samuel R. Bowman 1 2 Abstract The widespread public deployment of large lan- guage models (LLMs) in recent months has prompted a wave of new attention and engage- ment from advocates, policymakers, and scholars from many fields. This attention is a timely re- sponse to the many urgent questions that this tech- nology raises, but it can sometimes mis
This article will discuss building a simple neural network using JavaScript. However, let’s first check what deep neural networks and artificial neural networks are. Deep Neural Network and Artificial Neural Network Artificial Neural Networks (ANNs) and Deep Neural Networks (DNNs) are related concepts, but they are different. The inspiration behind these artificial neural networks for machine lear
All is Fair in Arb and MEV on Avalanche C-Chain - Daniel D. McKinnon
Note on generalizations: this blog post covers a large span of topics. Some MEV experts may object to some of the simplifications I made to make this story more understandable. Some casual readers may find this too complex to follow. I tried to find a happy medium but probably didn’t succeed. Let me know if you have any concrete suggestions for improvement. The email invitation Just months away fr
3行 手段が目的化していたため、UoPeopleに入学するのは見送った 入学を目指すプロセスにおいても学びは多かった Courseraで数学とCS勉強中 UoPeopleの入学見送り 下記のエントリで記した通り、アメリカのオンライン大学のUoPeopleに入学しコンピュータサイエンスを学ぶために、3月頃まで英語を集中的に勉強していた。 tmkk.hatenablog.com 結論としては、入学を見送ることにした。 Twitterやはてブのコメントではポジティブな反応も多く、中には応援してくれていた方もいたため、申し訳なく思う。 入学を見送るきっかけになったのは、目的を再考する機会が訪れたからだった。 目的の再考 今働いている会社では一週間に一度、チームのマネージャーと30分間話す1on1を実施している。 マネージャーにはUoPeopleに興味があることや、英語を勉強している旨は1月頃には伝
「Google Colab」で「distilabel」を試したので、まとめました。 1. distilabel「distilabel」は、LLMを使用してLLM用のデータセットを作成するためのAI Feadback (AIF) フレームワークです。 ・LLMの最も一般的なライブラリ・APIとの統合 (HuggingFace Transformers、OpenAI、vLLMなど) ・Self-Instruct、Preferenceデータセットなどの複数のタスクに対応 ・データセットを Argillaにエクスポートすることで、データ探索とさらなるアノテーションが容易に 2. セットアップGoogle Colabでのセットアップ手順は、次のとおりです。 (1) パッケージのインストール。 # パッケージのインストール !pip install distilabel[openai,argilla]
In this roundtable, which grew out of a conference on maritime strategy in the Indo-Pacific region sponsored jointly by the United States Naval War College, the Japan Maritime Self-Defense Forces Maritime Command and Staff College, and the Sasakawa Peace Foundation, our contributors examine growing Japanese defense capabilities and aspirations. The authors examine the impact of a more robust Japan
Hello quantum world! Google publishes landmark quantum supremacy claim
The Sycamore chip is composed of 54 qubits, each made of superconducting loops.Credit: Erik Lucero Scientists at Google say that they have achieved quantum supremacy, a long-awaited milestone in quantum computing. The announcement, published in Nature on 23 October, follows a leak of an early version of the paper five weeks ago, which Google did not comment on at the time. In a world first, a team
The sad state of property-based testing libraries Posted on Jul 2, 2024 Property-based testing is a rare example of academic research that has made it to the mainstream in less than 30 years. Under the slogan “don’t write tests, generate them” property-based testing has gained support from a diverse group of programming language communities. In fact, the Wikipedia page of the original property-bas
This is a comprehensive catalog of quantum algorithms. If you notice any errors or omissions, please email me at spj.jordan@gmail.com. (Alternatively, you may submit a pull request to the repository on github.) Although I cannot guarantee a prompt response, your help is appreciated and will be acknowledged. Algebraic and Number Theoretic Algorithms Algorithm: Factoring Speedup: Superpolynomial Imp
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The Haskell School of Music — From Signals to Symphonies — Paul Hudak Yale University Department of Computer Science Version 2.4 (February 22, 2012) i The Haskell School of Music — From Signals to Symphonies — Paul Hudak Yale University Department of Computer Science New Haven, CT, USA Version 2.4 (February 22, 2012) Copyright c � Paul Hudak January 2011 All rights reserved. No part of this public
ルパート王子の立方体 - Wikipedia
ルパート王子の立方体が通過できるだけの穴を開けた単位立方体。 幾何学におけるルパート王子の立方体（ルパートおうじのりっぽうたい、英: Prince Rupert's cube、名称はカンバーランド公ルパートに由来）とは、単位立方体（辺の長さが1の立方体）に貫通した穴を（2つ以上に分裂することなしに）通過できるような最大の立方体である。その1辺の長さは通過する相手である単位立方体より約6%長い。単位立方体に完全に含まれるような最大の正方形を求める問題と密接に関係しており、解も同じである[1][2][3]。 カンバーランド公ルパートが問うた元々の問題は「立方体を、それと同じ大きさのもう一つの立方体に貫通させられるか？」であった[4]。 解[編集] 各辺長が付された単位立方体の斜方投影。緑色の一点鎖線は穴（青色の破線）を通る単位正方形 （単位立方体の断面）。 単位立方体の隣接する2辺上に、共通の
notes.dvi
NOTES FOR MATH 635: TOPOLOGICAL QUANTUM FIELD THEORY KO HONDA The goal of this course is to define invariants of 3-manifolds and knots and representations of the mapping class group, using quantum field theory. We will follow Kohno, Conformal Field Theory and Topology, supplementing it with additional material to make it more accessible. The amount of mathematics that goes into defining these inva
IIT JAM Statistics Coaching Delhi | IIT JAM Stat Coaching Mumbai
IIT JAM Statistics The Mathematical Statistics (MS) test paper of IIT JAM Entrance Examination contains questions from both Maths and Statistics subjects. The question paper comprises of Mathematics (40% weightage) and Statistics (60% weightage). Deep Institute not only provides coaching and traning for the subject but also give study material (Researched and Developed by Our Experts) to all the s
Shi Zhengli - Wikipedia
Shi Zhengli (simplified Chinese: 石正丽; traditional Chinese: 石正麗; born 26 May 1964) is a Chinese virologist who researches SARS-like coronaviruses of bat origin. Shi directs the Center for Emerging Infectious Diseases at the Wuhan Institute of Virology (WIV). In 2017, Shi and her colleague Cui Jie discovered that the SARS coronavirus likely originated in a population of cave-dwelling horseshoe bats
111 A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT YIHAN CAO∗, Lehigh University & Carnegie Mellon University, USA SIYU LI, Lehigh University, USA YIXIN LIU, Lehigh University, USA ZHILING YAN, Lehigh University, USA YUTONG DAI, Lehigh University, USA PHILIP S. YU, University of Illinois at Chicago, USA LICHAO SUN, Lehigh University, USA Recen
How To Take Smart Notes: 10 Principles to Revolutionize Your Note-Taking and Writing - Forte Labs
How To Take Smart Notes: 10 Principles to Revolutionize Your Note-Taking and Writing I long ago stopped reading books on note-taking. They were always too vague and boring, full of platitudes that had little to do with the world outside academia. I especially avoided “how-to” style books on the subject. They would often list dozens of tips and tricks that had little to do with each other. There wa
An Introduction to Bayesian Data Analysis for Cognitive Science
An Introduction to Bayesian Data Analysis for Cognitive Science Bruno Nicenboim, Daniel Schad, and Shravan Vasishth 2024-03-11 Preface This book is intended to be a relatively gentle introduction to carrying out Bayesian data analysis and cognitive modeling using the probabilistic programming language Stan (Carpenter et al. 2017), and the front-end to Stan called brms (Bürkner 2019). Our target au
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GPT-3 Creative Fiction · Gwern.net
Testing in Production: the hard parts
Introduction to Decision Trees in Supervised Learning
An Introduction to Bayesian Network for Machine Learning
COVID-19 Is Transmitted Through Aerosols. We Have Enough Evidence, Now It Is Time to Act.
Eight Things to Know about Large Language Models
Building a Simple Artificial Neural Network in JavaScript
UoPeopleの入学を見送ってCourseraで勉強することにした - Velocity
Google Colab で distilabel を試す｜npaka
Policy Roundtable: The Future of Japanese Security and Defense - Texas National Security Review
The sad state of property-based testing libraries
Quantum Algorithm Zoo
A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT