Aho/Ullman Foundations of Computer Science This book has been taken out of print by W. H. Freeman. You are welcome to use it if you like. We believed in 1992 it was the way to introduce theory in Computer Science, and we believe that today. --- Al Aho and Jeff Ullman Index The Book | Materials | Gradiance | Errata PDF's of the Book PrefaceTable of ContentsChapter 1 Computer Science: The Mechanization of Abstraction Chapter 2 Iteration, Induction, and Recursion Chapter 3 The Running Time of Programs Chapter 4 Combinatorics and Probability Chapter 5 The Tree Data Model Chapter 6 The List Data Model Chapter 7 The Set Data Model Chapter 8 The Relational Data Model Chapter 9 The Graph Data Model Chapter 10 Patterns, Automata, and Regular Expressions Chapter 11 Recursive Description of Patterns Chapter 12 Propositional Logic Chapter 13 Using Logic to Design Computer Components Chapter 14 Predicate Logic Index Supplementary Materials Gradiance Package Available Errata Sheet We are going to try to maintain errata.
cryptanalysis - Recommended skills for a job in cryptology - Cryptography Stack Exchange It seems we have aligned interests. I'm also a university student (although I am a math/comp sci double major) looking to pursue a career in cryptography. To that end, I have been self-studying it for a while now. So, take what I say with a grain of salt. From what I can best tell, the requisite knowledge of computer science is entirely dependent on what you want to do with cryptography exactly. The reasons for this are many-fold. Further, you will need a strong knowledge of C, which sits so close to the hardware that it isn't too far a step away anyways. To that end, I would really recommend you pick up a minor in computer science if you are going to work with applied cryptography at all. Of course, to get very far in crypto, you will have to have a strong understanding of mathematics. The HAC lists probability theory, information theory, complexity theory, number theory, and abstract algebra as being introductory background material, and the rabbit hole just goes deeper and deeper.
The GNU General Public License v3.0 Version 3, 29 June 2007 Copyright © 2007 Free Software Foundation, Inc. < Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. Preamble The GNU General Public License is a free, copyleft license for software and other kinds of works. The licenses for most software and other practical works are designed to take away your freedom to share and change the works. When we speak of free software, we are referring to freedom, not price. To protect your rights, we need to prevent others from denying you these rights or asking you to surrender the rights. For example, if you distribute copies of such a program, whether gratis or for a fee, you must pass on to the recipients the same freedoms that you received. Developers that use the GNU GPL protect your rights with two steps: (1) assert copyright on the software, and (2) offer you this License giving you legal permission to copy, distribute and/or modify it. 0. 1. 2.
eBouquin | Toute l'actualité sur le livre et la lecture numérique SkullSecurity » Adventures In Security Ferdy on Films Comprendre et appliquer Sun Tzu 80+ Best Free Hacking Tutorials | Resources to Become Pro Hacker Learning to become hacker is not as easy as learning to become a software developer. I realized this when I started looking for learning resources for simple hacking people do. Even to start doing the simplest hack on own, a hacker requires to have in depth knowledge of multiple topics. Some people recommend minimum knowledge of few programming languages like C, Python, HTML with Unix operating system concepts and networking knowledge is required to start learning hacking techniques. Though knowing a lot of things is required, it is not really enough for you to be a competent and successful hacker. If you are thinking about ethical hacking as a career option, you may need to be prepared for a lot of hard/smart work. A lot of people (including me before doing research for this article) think that they can become a hacker using some free hacking tools available on web. Below are some really useful hacking tutorials and resources you may want to explore in your journey of learning to hack
Coffee Making: Introduction Next Previous Contents 1. Introduction For a long time humanity has been wondering how could a computer make coffee... People need coffee to wake up, and stay awake for a long time in front of the computer. The main trick is interfacing a coffee machine to the computer, so that it can be controlled by software. At first, it demonstrates an ON/OFF switch implemented as an electronic circuit which controls the coffee-machine's power supply. This HOWTO was initially written as part of a debate in the mailing list linux-greek-users, on whether linux can make coffee or not. Enjoy. 1.1 Copyright Copyright © 2004-08-29 by Fotis Georgatos. to copy, distribute, display, and perform the workto make derivative worksto make commercial use of the work Under the following conditions: Attribution. 1.2 Disclaimer Use the information in this document at your own risk. All copyrights are owned by their owners, unless specifically noted otherwise. 1.3 Version
OpenGL Projects FOAF Wiki Natural language processing tutorial - Vik's Blog Introduction This will serve as an introduction to natural language processing. I adapted it from slides for a recent talk at Boston Python. We will go from tokenization to feature extraction to creating a model using a machine learning algorithm. The goal is to provide a reasonable baseline on top of which more complex natural language processing can be done, and provide a good introduction to the material. The examples in this code are done in R, but are easily translatable to other languages. Training set example Let's say that I wanted to give a survey today and ask the following question: Why do you want to learn about machine learning? The responses might look like this: ## 1 I like solving interesting problems. ## 2 What is machine learning? Let's say that the survey also asks people to rate their interest on a scale of 0 to 2. We would now have text and associated scores: First steps What is the algorithm doing? Tokenization Let's tokenize the first survey response: Bag of words model