Coding theory | Information theory | Finite fields
In computer networking, linear network coding is a program in which intermediate nodes transmit data from source nodes to sink nodes by means of linear combinations. Linear network coding may be used to improve a network's throughput, efficiency, and scalability, as well as reducing attacks and eavesdropping. The nodes of a network take several packets and combine for transmission. This process may be used to attain the maximum possible information flow in a network. It has been proven that, theoretically, linear coding is enough to achieve the upper bound in multicast problems with one source. However linear coding is not sufficient in general; even for more general versions of linearity such as convolutional coding and . Finding optimal coding solutions for general network problems with arbitrary demands remains an open problem. (Wikipedia).
This video is a brief introduction to linear codes: dimensions, G (generating matrix), H (parity check matrix), their forms. Also gives an example of how to convert between G and H. Here is the formal definition of a Linear Code: A linear code of dimension k and length n over a field
From playlist Cryptography and Coding Theory
Basic linear algebra for deep learning
This a series for healthcare professionals and anyone else interested in learning how to create deep neural networks. In this video tutorial I demonstrate the very basic principles of linear algebra. For a more comprehensive view of the topic watch my playlist here: https://www.youtube.c
From playlist Introduction to deep learning for everyone
Determining if a vector is a linear combination of other vectors
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Determining if a vector is a linear combination of other vectors
From playlist Linear Algebra
Linear Algebra for Computer Scientists. 7. Linear Combinations of Vectors
This computer science video is one of a series on linear algebra for computer scientists. In this video you will learn about linear combinations of vectors, that is, you will learn how to create new vectors by scaling then adding other vectors together. You will also learn that some sets
From playlist Linear Algebra for Computer Scientists
what is linear and non linear in machine learning, deep learning
what is linear and non linear in machine learning and deep learning? you will have clear understanding after watching this video. all machine learning youtube videos from me, https://www.youtube.com/playlist?list=PLVNY1HnUlO26x597OgAN8TCgGTiE-38D6
From playlist Machine Learning
This is part of an online course on beginner/intermediate linear algebra, which presents theory and implementation in MATLAB and Python. The course is designed for people interested in applying linear algebra to applications in multivariate signal processing, statistics, and data science.
From playlist Linear algebra: theory and implementation
Powered by https://www.numerise.com/ Formulating a linear programming problem
From playlist Linear Programming - Decision Maths 1
Linear algebra is the branch of mathematics concerning linear equations such as linear functions and their representations through matrices and vector spaces. Linear algebra is central to almost all areas of mathematics. Topic covered: Vectors: Basic vectors notation, adding, scaling (0:0
From playlist Linear Algebra
Spotlight Talks Pt1 - Zhiyuan Li, John Zarka, Stanislav Fort
Workshop on Theory of Deep Learning: Where next? Topic: Spotlight Talks: Zhiyuan Li, John Zarka, Stanislav Fort Speaker: Various Date: October 18, 2019 For more video please visit http://video.ias.edu
From playlist Mathematics
Nexus Trimester - Young Han Kim (UCSD)
Fundamental Inequalities and Capacity Upper Bounds Young-Han Kim (UCSD) February 17, 2016 Abstract : Index coding is a canonical problem in network information theory that provides a simple yet powerful platform to develop new coding techniques and capacity bounds. We discuss upper bounds
From playlist Nexus Trimester - 2016 - Fundamental Inequalities and Lower Bounds Theme
10.6: Neural Networks: Matrix Math Part 1 - The Nature of Code
In this video, I introduce the idea of "Linear Algebra" and explore the matrix math required for a simple neural network library. Next video: https://youtu.be/n6q9D2wd1bE This video is part of Chapter 10 of The Nature of Code (http://natureofcode.com/book/chapter-10-neural-networks/) Th
From playlist Session 4 - Neural Networks - Intelligence and Learning
Nexus Trimester - Babak Hassibi (Caltech)
Simple Algorithms and Guarantees for Low Rank Matrix Completion over ... Babak Hassibi (Caltech) February 23, 2016 Abstract: Let [Math Processing Error] be a [Math Processing Error]-by-[Math Processing Error] matrix with entries in [Math Processing Error] and rank [Math Processing Error]
From playlist Nexus Trimester - 2016 - Fundamental Inequalities and Lower Bounds Theme
Live Stream #98: Starting Series on Neural Networks
In this live stream, I begin the long process of building a neural network library. I cover the concept of a "multi-layer perceptron" as well as linear algebra / matrix math. Edited videos coming soon! 30:13 - Intro to Neural Networks 41:38 - Multilayered Perceptron Part 1 1:08:18 - Mult
From playlist Live Stream Archive
Nexus trimester - Michael Langberg (SUNY at Buffalo)
A reductionist view of network information theory Michael Langberg (SUNY at Buffalo) February 08, 2016 Abstract: The network information theory literature includes beautiful results describing codes and performance limits for many different networks. While common tools and themes are evi
From playlist Nexus Trimester - 2016 - Distributed Computation and Communication Theme
Why Deep Q Learning Needs A Target Network and Replay Memory | Course Excerpt For Cyber Monday
The two biggest innovations in deep Q learning were the introduction of the target network and the replay memory. One would think that simply bolting a deep neural network to the Q learning algorithm would be enough for a robust deep Q learning agent, but that isn't the case. In this video
From playlist Deep Reinforcement Learning Tutorials - All Videos
Build PyTorch CNN - Object Oriented Neural Networks
Build a convolutional neural network with PyTorch for computer vision and artificial intelligence. References: Jeremy: https://youtu.be/3jl2h9hSRvc?t=5106 ๐๐ฆ VIDEO SECTIONS ๐ฆ๐ 00:00 Welcome to DEEPLIZARD - Go to deeplizard.com for learning resources 00:30 Help deeplizard add video time
From playlist PyTorch - Python Deep Learning Neural Network API
CNN Weights - Learnable Parameters in PyTorch Neural Networks
In this post, we'll be exploring the inner workings of PyTorch, Introducing more OOP concepts, convolutional and linear layer weight tensors, matrix multiplication for deep learning and more! Rachel's Ted Talk: https://youtu.be/LqjP7O9SxOM ๐๐ฆ VIDEO SECTIONS ๐ฆ๐ 00:00 Welcome to DEEPLIZAR
From playlist PyTorch - Python Deep Learning Neural Network API
How to calculate Linear Regression using R. http://www.MyBookSucks.Com/R/Linear_Regression.R http://www.MyBookSucks.Com/R Playlist http://www.youtube.com/playlist?list=PLF596A4043DBEAE9C
From playlist Linear Regression.