In information theory, information dimension is an information measure for random vectors in Euclidean space, based on the normalized entropy of finely quantized versions of the random vectors. This concept was first introduced by Alfréd Rényi in 1959. Simply speaking, it is a measure of the fractal dimension of a probability distribution. It characterizes the growth rate of the Shannon entropy given by successively finer discretizations of the space. In 2010, Wu and Verdú gave an operational characterization of Rényi information dimension as the fundamental limit of almost lossless data compression for analog sources under various regularity constraints of the encoder/decoder. (Wikipedia).
(IC 1.6) A different notion of "information"
An informal discussion of the distinctions between our everyday usage of the word "information" and the information-theoretic notion of "information". A playlist of these videos is available at: http://www.youtube.com/playlist?list=PLE125425EC837021F Attribution for image of TV static:
From playlist Information theory and Coding
Dimensions (1 of 3: The Traditional Definition - Directions)
More resources available at www.misterwootube.com
From playlist Exploring Mathematics: Fractals
Chapter 5 of the Dimensions series. See http://www.dimensions-math.org for more information. Press the 'CC' button for subtitles.
From playlist Dimensions
Chapter 1 of the Dimensions series. See http://www.dimensions-math.org for more information. Press the 'CC' button for subtitles.
From playlist Dimensions
Chapter 2 of the Dimensions series. See http://www.dimensions-math.org for more information. Press the 'CC' button for subtitles.
From playlist Dimensions
Giulio Tononi - What is Information?
Free access to Closer to Truth's library of 5,000 videos: http://bit.ly/2UufzC7 Information is a common word but has technical meanings so important that our entire world depends on them. What are the kinds of information? How about the scientific definitions of information? How does info
From playlist What is Information? - CTT Interview Series
Chapter 6 of the Dimensions series. See http://www.dimensions-math.org for more information. Press the 'CC' button for subtitles.
From playlist Dimensions
Chapter 4 of the Dimensions series. See http://www.dimensions-math.org for more information. Press the 'CC' button for subtitles.
From playlist Dimensions
4 Dimensions Of Service Management | ITIL 4 Foundation Training: The Four Dimensions | Simplilearn
🔥 ITIL® 4 Foundation Certification Training Course: https://www.simplilearn.com/it-service-management/itil-foundation-training?utm_campaign=ITIL_YqT7AXaYlyA&utm_medium=DescriptionFirstFold&utm_source=youtube This video on the 4 Dimensions of Service Management will help you understand Se
From playlist ITIL Training Videos [2022 Updated]
RailsConf 2017: Reporting on Rails - ActiveRecord and ROLAP Working Together by Tony Drake
RailsConf 2017: Reporting on Rails - ActiveRecord and ROLAP Working Together by Tony Drake It'll happen eventually. Someone will come down with a feature request for your app to "create dashboards and reporting on our data". So how do you go about doing it? What parts of your database sho
From playlist RailsConf 2017
Analisis de componentes principales (PCA) - Nuevo link: https://www.youtube.com/watch?v=7My_PBhxeP4
ANUNCIO: Este video pronto se movera aca: https://www.youtube.com/watch?v=7My_PBhxeP4 Los videos en español pronto se moverán a este canal: https://www.youtube.com/channel/UCvnzQ7-7MrsC6AVo5LxnQWw Link to video in English: https://www.youtube.com/watch?v=g-Hb26agBFg Este video explica un
From playlist Machine learning en espanol
Entropy-Based Bounds on Dimension Reduction in L_1 - Oded Regev
Oded Regev CNRS-ENS-Paris and Tel Aviv University November 28, 2011 For more videos, visit http://video.ias.edu
From playlist Mathematics
Linformer: Self-Attention with Linear Complexity (Paper Explained)
Transformers are notoriously resource-intensive because their self-attention mechanism requires a squared number of memory and computations in the length of the input sequence. The Linformer Model gets around that by using the fact that often, the actual information in the attention matrix
From playlist Papers Explained
Entanglement entropy, quantum field theory, and holography by Matthew Headrick
26 December 2016 to 07 January 2017 VENUE : Madhava Lecture Hall, ICTS, Bengaluru Information theory and computational complexity have emerged as central concepts in the study of biological and physical systems, in both the classical and quantum realm. The low-energy landscape of classic
From playlist US-India Advanced Studies Institute: Classical and Quantum Information
Deriving Hawking's most famous equation: What is the temperature of a black hole?
Black holes are perhaps the most enigmatic objects in the universe. Popularised in movies and science fiction, they evoke the magic and mystery of our universe and provide inspiration for those looking to make their mark in the world of academic physics. But what exactly is a black hole? A
From playlist Relativity
Machine Learning for Signal Processing: Data Compression and Denoising
In this meetup, we will understand how to use machine learning tools for signal processing. In particular: data compression and noise removal. To do so, we will discuss Principal Component Analysis (PCA) and explore how linear algebra can be used for these and other applications. Presente
From playlist Fundamentals of Machine Learning
Data Warehouse Concepts | Data Warehouse Tutorial | Data Warehouse Architecture | Edureka
***** Data Warehousing & BI Training: https://www.edureka.co/data-warehousing-and-bi ***** This tutorial on data warehouse concepts will tell you everything you need to know in performing data warehousing and business intelligence. The various data warehouse concepts explained in this vide
From playlist Data Warehousing Tutorial Videos
Exploring 3 Dimensions - Abigail Thompson
Friends Lunch with a Member: December 4, 2015 "Exploring 3 Dimensions" Abigail Thompson More videos on http://video.ias.edu
From playlist Friends of the Institute
The concept of “dimension” in measured signals
This is part of an online course on covariance-based dimension-reduction and source-separation methods for multivariate data. The course is appropriate as an intermediate applied linear algebra course, or as a practical tutorial on multivariate neuroscience data analysis. More info here:
From playlist Dimension reduction and source separation