Pseudorandom Number Generation and Stream Ciphers
Fundamental concepts of Pseudorandom Number Generation are discussed. Pseudorandom Number Generation using a Block Cipher is explained. Stream Cipher & RC4 are presented.
From playlist Network Security
Summary - Applied Cryptography
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
From playlist Applied Cryptography
Number Theory: Part 2: Chinese Remainder Theorem
Chinese Remainder Theorem is presented. Discrete Logarithms are analyzed.
From playlist Network Security
Cipher Block Chaining Mode - Applied Cryptography
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
From playlist Applied Cryptography
Block Ciphers and Data Encryption Standard (DES): Part 2
Fundamental concepts of Block Cipher Design Principles are discussed. Differential cryptanalysis and linear cryptanalysis are explained.
From playlist Network Security
Cryptography and Network Security by Prof. D. Mukhopadhyay, Department of Computer Science and Engineering, IIT Kharagpur. For more details on NPTEL visit http://nptel.iitm.ac.in
From playlist Computer - Cryptography and Network Security
Blockchain explained by analogy
A blockchain involves a shared data structure and protocols that are hard to mess with. Here I present a system that may give some intuition for main features of blockchain technology, as used for cryptocurrencies like Bitcoin or Ethereum. Summary: To this end, I use a simple method to tr
From playlist Crypto
Block Ciphers and Data Encryption Standard (DES) - Part 1
Fundamental concepts of Block Cipher Design Principles are discussed. DES is presented. Differential and linear cryptanalysis are explained . Block Cipher Principles Data Encryption Standard (DES) Differential and Linear Cryptanalysis Block Cipher Design Principles
From playlist Network Security
Masayuki Ohzeki: "Quantum annealing and machine learning - new directions of quantum"
Machine Learning for Physics and the Physics of Learning 2019 Workshop IV: Using Physical Insights for Machine Learning "Quantum annealing and machine learning - new directions of quantum" Masayuki Ohzeki - Tohoku University Abstract: Quantum annealing is a generic solver of combinator
From playlist Machine Learning for Physics and the Physics of Learning 2019
Grokking: Generalization beyond Overfitting on small algorithmic datasets (Paper Explained)
#grokking #openai #deeplearning Grokking is a phenomenon when a neural network suddenly learns a pattern in the dataset and jumps from random chance generalization to perfect generalization very suddenly. This paper demonstrates grokking on small algorithmic datasets where a network has t
From playlist Papers Explained
Lecture 11/16 : Hopfield nets and Boltzmann machines
Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] 11A Hopfield Nets 11B Dealing with spurious minima in Hopfield Nets 11C Hopfield Nets with hidden units 11D Using stochastic units to improve search 11E How a Boltzmann Machine models data
From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]
Lecture 11.5 — How a Boltzmann machine models data [Neural Networks for Machine Learning]
Lecture from the course Neural Networks for Machine Learning, as taught by Geoffrey Hinton (University of Toronto) on Coursera in 2012. Link to the course (login required): https://class.coursera.org/neuralnets-2012-001
From playlist [Coursera] Neural Networks for Machine Learning — Geoffrey Hinton
ShmooCon 2014: Introducing DARPA's Cyber Grand Challenge
For more information visit: http://bit.ly/shmooc14 To download the video visit: http://bit.ly/shmooc14_down Playlist Shmoocon 2014: http://bit.ly/shmooc14_pl Speaker: Mike Walker Could a purpose-built supercomputer play DEFCON capture the flag?
From playlist ShmooCon 2014
Lecture 11E : How a Boltzmann Machine models data
Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] Lecture 11E : How a Boltzmann Machine models data
From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]
Binary Text Classification | The Problem | Dan Brown v Oscar Wilde (Neural Nets for DH 11a)
In this video, we begin exploring our first binary text classification problem. I explain what a binary text classification problem is and then discuss the problem. I also discuss generally the solution that we will employ over the next few videos. If you enjoy this video, please subscrib
From playlist Binary Text Classification (Dan Brown v. Oscar Wilde)
Lecture 14/16 : Deep neural nets with generative pre-training
Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] 14A Learning layers of features by stacking RBMs 14B Discriminative fine-tuning for DBNs 14C What happens during discriminative fine-tuning? 14D Modeling real-valued data with an RBM 14E RBMs are Infinite Sigmoid Beli
From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]
Kaggle Reading Group: Deep Learning for Symbolic Mathematics
This week we start in on "Deep Learning for Symbolic Mathematics", (anonymous, submitted to ICLR 2020). You can find a link to the paper here: https://openreview.net/forum?id=S1eZYeHFDS SUBSCRIBE: https://www.youtube.com/c/kaggle?sub_... About Kaggle: Kaggle is the world's largest commun
From playlist Kaggle Reading Group | Kaggle
Longest Block Chain - Applied Cryptography
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
From playlist Applied Cryptography