Automata presentation turing machine programming techniques
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Turing machine-TOC
The document discusses Turing machines. It begins by introducing Alan Turing as the father of the Turing machine model. A Turing machine is a general model of a CPU that can manipulate data through a finite set of states and symbols. It consists of a tape divided into cells that can be read from and written to by a tape head. The tape head moves left and right across the cells. The document then provides examples of constructing Turing machines to accept specific languages, such as the language "aba" and checking for palindromes of even length strings. Transition tables are used to represent the state transitions of the Turing machines.
Turing machine
It`ll help you in understanding the working of Turing Machine thoroughly with a good and simple example. Advantages of Turing Machine over FA.
Computer Network notes (handwritten) UNIT 1
Introduction of computer network, layered architecture, topology, guided and unguided media, signals, multiplexing, OSI vs TCP/IP , IP address, TCP , UDP, DHCP, DNS, HTTP, etc.
AI 7 | Constraint Satisfaction Problem
The document provides an overview of constraint satisfaction problems (CSPs). It defines a CSP as consisting of variables with domains of possible values, and constraints specifying allowed value combinations. CSPs can represent many problems using variables and constraints rather than explicit state representations. Backtracking search is commonly used to solve CSPs by trying value assignments and backtracking when constraints are violated.
Multi dimensional turing machine
Multi-Dimensional TURING MACHINE
WHAT IS TURING MACHINE
WHY WE NEED
HOW IT WORKS
REFERENCE NESO ACADEMY
Chess board problem(divide and conquer)
The document describes the divide and conquer algorithm for solving the defective chessboard problem. The problem is to cover a chessboard of size n x n, where n is a power of 2, with L-shaped tiles (trominoes), except for one defective square. The algorithm divides the board recursively into equal half-sized subboards until reaching boards of size 2x2, which can be covered with at most one tile. It then combines the solutions to the subproblems to construct a solution for the original board. The time complexity of this divide and conquer algorithm is O(n^2).
Turing machine
This document provides an overview of the Turing machine. It describes the Turing machine as an abstract computational model invented by Alan Turing in 1936. A Turing machine consists of an infinite tape divided into cells, a tape head that reads and writes symbols on the tape, and a state table that governs the machine's behavior. The document then explains the formal definition of a Turing machine, provides an example of how it works, discusses properties like decidability and recognizability, and covers modifications like multi-tape and non-deterministic Turing machines. It concludes by discussing the halting problem and explaining how Turing machines demonstrate the power and applications of computational theory.
Mealy and moore machine
Finite automaton discussed so far, is just associated with the RE or the language.
There is a question whether does there exist an FA which generates an output string corresponding to each input string ? The answer is yes. Such machines are called machines with output.
There are two types of machines with output.
Moore machine
Mealy machine
Equivalent machines Two machines are said to be equivalent if they print the same output string when the same input string is run on them.
Recommended
Turing machine-TOC
The document discusses Turing machines. It begins by introducing Alan Turing as the father of the Turing machine model. A Turing machine is a general model of a CPU that can manipulate data through a finite set of states and symbols. It consists of a tape divided into cells that can be read from and written to by a tape head. The tape head moves left and right across the cells. The document then provides examples of constructing Turing machines to accept specific languages, such as the language "aba" and checking for palindromes of even length strings. Transition tables are used to represent the state transitions of the Turing machines.
Turing machine
It`ll help you in understanding the working of Turing Machine thoroughly with a good and simple example. Advantages of Turing Machine over FA.
Computer Network notes (handwritten) UNIT 1
Introduction of computer network, layered architecture, topology, guided and unguided media, signals, multiplexing, OSI vs TCP/IP , IP address, TCP , UDP, DHCP, DNS, HTTP, etc.
AI 7 | Constraint Satisfaction Problem
The document provides an overview of constraint satisfaction problems (CSPs). It defines a CSP as consisting of variables with domains of possible values, and constraints specifying allowed value combinations. CSPs can represent many problems using variables and constraints rather than explicit state representations. Backtracking search is commonly used to solve CSPs by trying value assignments and backtracking when constraints are violated.
Multi dimensional turing machine
Multi-Dimensional TURING MACHINE
WHAT IS TURING MACHINE
WHY WE NEED
HOW IT WORKS
REFERENCE NESO ACADEMY
Chess board problem(divide and conquer)
The document describes the divide and conquer algorithm for solving the defective chessboard problem. The problem is to cover a chessboard of size n x n, where n is a power of 2, with L-shaped tiles (trominoes), except for one defective square. The algorithm divides the board recursively into equal half-sized subboards until reaching boards of size 2x2, which can be covered with at most one tile. It then combines the solutions to the subproblems to construct a solution for the original board. The time complexity of this divide and conquer algorithm is O(n^2).
Turing machine
This document provides an overview of the Turing machine. It describes the Turing machine as an abstract computational model invented by Alan Turing in 1936. A Turing machine consists of an infinite tape divided into cells, a tape head that reads and writes symbols on the tape, and a state table that governs the machine's behavior. The document then explains the formal definition of a Turing machine, provides an example of how it works, discusses properties like decidability and recognizability, and covers modifications like multi-tape and non-deterministic Turing machines. It concludes by discussing the halting problem and explaining how Turing machines demonstrate the power and applications of computational theory.
Mealy and moore machine
Finite automaton discussed so far, is just associated with the RE or the language.
There is a question whether does there exist an FA which generates an output string corresponding to each input string ? The answer is yes. Such machines are called machines with output.
There are two types of machines with output.
Moore machine
Mealy machine
Equivalent machines Two machines are said to be equivalent if they print the same output string when the same input string is run on them.
14 relationship between processes
This document discusses process management and cooperation between processes. It defines independent and cooperating processes, with independent processes unable to affect or be affected by other processes, while cooperating processes can. Reasons for process cooperation include information sharing, speeding up computation by dividing tasks, modularity, and convenience. The producer-consumer problem is provided as an example of cooperating processes, where a producer process generates items for a consumer process to use.
Turing machine
This document describes a report submitted by three students - Sonam Lanjewar, Sumbul Fatima Khan, and Aafaqueahmad Khan - on the theoretical foundations of computer science. It focuses on Turing machines, introducing them as a simple mathematical model of a computer conceived by Alan Turing in 1936. Key aspects of a Turing machine are described, including its finite tape divided into cells, read/write head, state register, and finite table of instructions defining its behavior based on the current state and symbol read. Formal definitions of Turing machines and multi-tape Turing machines are also provided. The document aims to inform the reader about Turing machines and their role in modeling computational capability.
System calls
This presentation discusses system calls and provides an overview of their key aspects:
System calls provide an interface between processes and the operating system. They allow programs to request services from the OS like reading/writing files. There are different methods of passing parameters to the OS, such as via registers, parameter blocks, or pushing to the stack. System calls fall into categories including process control, file management, device management, information maintenance, and communication. An example is given of how system calls would be used in a program to copy data between two files.
Turing Machine
This presentation discusses Turing machines. It introduces Turing machines as a simple mathematical model of a computer that models computing capability. A Turing machine is represented as a 7-tuple that includes the finite set of states, input symbols, tape symbols, transition function, start state, blank symbol, and accepting states. The presentation covers various topics such as the Turing machine model, uses of Turing machines as language recognizers and generators, transition functions, instantaneous descriptions, variations of Turing machines, recursive and recursively enumerable languages, universal Turing machines, and properties of Turing machines.
Lecture: Automata
The document provides an introduction to automata theory and finite state automata (FSA). It defines an automaton as an abstract computing device or mathematical model used in computer science and computational linguistics. The reading discusses pioneers in automata theory like Alan Turing and his development of Turing machines. It then gives an overview of finite state automata, explaining concepts like states, transitions, alphabets, and using a example of building an FSA for a "sheeptalk" language to demonstrate these components.
Csma cd and csma-ca
The document discusses MAC layer protocols, specifically CSMA/CD and CSMA/CA.
CSMA/CD is used for wired networks and works by having nodes listen to check if the medium is free before transmitting. If a collision is detected, transmission stops and resumes after a backoff time.
CSMA/CA is used for wireless networks and aims to avoid collisions through the use of request to send, clear to send, and acknowledgement frames exchanged between nodes, rather than detecting collisions.
Both protocols reduce collisions compared to simple CSMA, but CSMA/CA is less efficient and cannot completely solve collisions in wireless networks due to issues like hidden terminals.
pushdown automata
The document describes pushdown automata (PDA). A PDA has a tape, stack, finite control, and transition function. It accepts or rejects strings by reading symbols on the tape, pushing/popping symbols on the stack, and changing state according to the transition function. The transition function defines the possible moves of the PDA based on the current state, tape symbol, and stack symbol. If the PDA halts in a final state with an empty stack, the string is accepted. PDAs can recognize any context-free language. Examples are given of PDAs for specific languages.
Turing Machine
The document discusses Turing machines and their properties. It introduces the Church-Turing thesis that any problem that can be solved by an algorithm can be modeled by a Turing machine. It then describes different types of Turing machines, such as multi-track, nondeterministic, two-way, multi-tape, and multidimensional Turing machines. The document provides examples of Turing machines that accept specific languages and evaluate mathematical functions through their transition tables and diagrams.
Multi Head, Multi Tape Turing Machine
A multi-head Turing machine has a single tape with multiple heads that can read and write to the tape independently. Each head can move left, right, or stay in the same position. This type of Turing machine is as powerful as a standard single-tape Turing machine.
The halting problem asks if it is possible to determine if a Turing machine will halt or run infinitely given its program and input. It is proven to be unsolvable - there is no general algorithm that can correctly determine if all Turing machine programs will halt for all inputs.
Back propagation
The document discusses artificial neural networks and backpropagation. It provides an overview of backpropagation algorithms, including how they were developed over time, the basic methodology of propagating errors backwards, and typical network architectures. It also gives examples of applying backpropagation to problems like robotics, space robots, handwritten digit recognition, and face recognition.
Universal turing coastus
The document discusses Turing machines and languages. It introduces the concept of a universal Turing machine, which can simulate any other Turing machine. It then discusses countable and uncountable sets, proving that the set of all Turing machines and the set of rational numbers are countable, while the power set of any infinite countable set is uncountable. This implies that the set of all possible languages is uncountable, but the set of languages accepted by Turing machines is countable. Therefore, there must exist at least one language that is not accepted by any Turing machine.
Chomsky Normal Form
This document discusses the process of converting a context-free grammar (CFG) into Chomsky normal form (CNF) in multiple steps. It first recalls the definition of CNF and the theorem that every context-free language minus the empty string has a CFG in CNF. It then outlines the steps to convert a CFG to CNF: 1) remove epsilon productions, 2) remove unit rules, 3) break productions with more than two variables into chains of productions with two variables, and 4) ensure all productions are in the forms A->BC or A->a. The document provides two examples showing the full conversion process.
Turing machine by_deep
This document provides an overview of Turing machines. It introduces Turing machines as a simple mathematical model of a computer proposed by Alan Turing in 1936. A Turing machine consists of a tape divided into cells, a read/write head, finite states, and a transition function. The transition function defines how the machine moves between states and reads/writes symbols on the tape based on its current state and tape symbol. Turing machines can accept languages and compute functions. Variations include multi-tape, non-deterministic, multi-head, offline, multi-dimensional, and stationary-head Turing machines. The properties of Turing machines include their ability to recognize any language generated by a phrase-structure grammar and the Church
Water jug problem ai part 6
here is a presentation showing how problems are representated in Artificial intelligence.must watch my youtube videos on my channel techiseasy.
TOC 7 | CFG in Chomsky Normal Form
This Slide contains:
- CFG definition
- CNF definition
- CNF rules and violations
- CFG to CNF conversion
- Practice problems list
backpropagation in neural networks
Backpropagation is the algorithm that is used to train modern feed-forwards neural nets. This ppt aims to explain it succinctly.
Directory structure
- Directory structures organize files in a storage system and contain metadata about each file's name, location, size, and type. They allow operations like creating, searching, deleting, listing, and renaming files.
- Early systems used single-level directories with one list of all files, but this does not allow multiple files with the same name or grouping of files.
- Modern systems commonly use tree-structured directories that allow nesting files into subdirectories, making searching more efficient and allowing grouping of similar files. Directories can also be connected in acyclic graphs to enable sharing of files between directories through links.
Case based reasoning
Case-based reasoning (CBR) is a learning paradigm that classifies new instances by analyzing similar past instances, ignoring dissimilar ones. It uses symbolic representations rather than real-valued points. CBR has been applied to conceptual design, legal reasoning, and planning by reusing solutions to similar past problems. The CADET system employs CBR to design simple mechanical devices like faucets, using a library of 75 past designs. It represents functions with qualitative relationships between input and output water levels and temperatures. CADET searches the library for exact functional description matches or partial subgraph matches to suggest solutions for new design problems.
Np cooks theorem
The document discusses the theory of NP-completeness. It begins by defining the complexity classes P, NP, NP-hard, and NP-complete. It then explains the concepts of reduction and how none of the NP-complete problems can be solved in polynomial time deterministically. The document provides examples of NP-complete problems like satisfiability (SAT), vertex cover, and the traveling salesman problem. It shows how nondeterministic algorithms can solve these problems and how they can be transformed into SAT instances. Finally, it proves that SAT is the first NP-complete problem by showing it is in NP and NP-hard.
DBMS Unit - 3 - Relational query languages
The passage discusses the benefits of regular exercise for both physical and mental health. It notes that working out helps reduce stress and anxiety while boosting mood and self-esteem. The article recommends starting slowly with light exercise and gradually increasing intensity and duration to reap these rewards over the long run.
Word2vec and Friends
Word embeddings have received a lot of attention since some Tomas Mikolov published word2vec in 2013 and showed that the embeddings that the neural network learned by “reading” a large corpus of text preserved semantic relations between words. As a result, this type of embedding started being studied in more detail and applied to more serious NLP and IR tasks such as summarization, query expansion, etc… More recently, researchers and practitioners alike have come to appreciate the power of this type of approach and have started a cottage industry of modifying Mikolov’s original approach to many different areas.
In this talk we will cover the implementation and mathematical details underlying tools like word2vec and some of the applications word embeddings have found in various areas. Starting from an intuitive overview of the main concepts and algorithms underlying the neural network architecture used in word2vec we will proceed to discussing the implementation details of the word2vec reference implementation in tensorflow. Finally, we will provide a birds eye view of the emerging field of “2vec" (dna2vec, node2vec, etc...) methods that use variations of the word2vec neural network architecture.
This (long) version of the Tutorial was presented at #O'Reilly AI 2017 in San Francisco. See https://bmtgoncalves.github.io/word2vec-and-friends/ for further details.
Understanding the History of EncryptionUnderstanding the
Understanding the History of Encryption
Understanding the History of Encryption
Few facts -
Encryption has been around for a very long time. Instead of explaining the History of Cryptography, I am going to recommend a very good book by David Khan called "The Code Breakers" , published in 1996.
Instead of starting with early encryption algorithm, I am going to used two interesting encryption algorithms that will capture manu of the concepts that Khan outline in his book. The first algorithm is Character Substitution. Remember, the problem we are trying to solve is "how do we secure communications?". An example is - Suppose Alice and Bob wants to communicate securely using Eve is an attacker that wants to eavesdrop on the conversation. In order to communicate securely, Alice and Bob are going to share a secret key, which we are going to call K. Eve does not know anything about the key K.
Alice, Bob and even Eve know the algorithm (say character substitution). We all have played the substitution game in School. A is map to C and B is map to X and C is map to W and so on and so forth. So, if EVE gets the mapping and the ciphered text, she will be able to decipher the secret. Let supposed Eve gets the ciphered text but not the mapping (key). It will be difficult to figure the key out.
Assuming we have 26 letters, how many possibilities are there --> guess?
1. 26
2. 26X26
3. 26! (factorial)
4. 2^26
The answer is 26! which is about 2^88 if I remember correctly, which is a very large sample space. I hope you understand where I got the 26!. Think of one mapping and now think of another mapping and so on.
What is factorial? 1! = 1, 2! = 1X2 = 2, 3! = 1X2X3 = 6 and so on.
When I said 2^88, it is a good key since it is using 88 bits. Note that I am not saying it is great.
What I want you to do is to think about how you would break a substitution cipher? It is not hard but it needs some imagination.
The Substitution Cipher and How to break the Cipher
Breaking The Substitution Cipher
One way to break a substitution cipher is to use frequency analysis. Let's focus on the English language for now but the process works the same way for other languages. The character "e" occurs 12.7% in the Eglish language and the letter "t" occurs 9.35% and the letter "a" is 8.2% of the time. So, you can count each character in the ciphered text, compute its frequency and start replacing it. After you complete the first say 4 characters, you may want to change your strategy. Can you explain why you want to stop after the first 4 characters? The two-character "of" is about 4.16% and "to" is about 2%. Now try three characters frequency.
After a period of time, you will be able to decipher most of the text. I am going to ask you to do your first significant homework using frequency analysis.
One possibility for the substitution cipher:
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Y
Z
↕
↕
↕
↕
↕
↕
↕
↕
↕
↕
...
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This document discusses process management and cooperation between processes. It defines independent and cooperating processes, with independent processes unable to affect or be affected by other processes, while cooperating processes can. Reasons for process cooperation include information sharing, speeding up computation by dividing tasks, modularity, and convenience. The producer-consumer problem is provided as an example of cooperating processes, where a producer process generates items for a consumer process to use.
Turing machine
This document describes a report submitted by three students - Sonam Lanjewar, Sumbul Fatima Khan, and Aafaqueahmad Khan - on the theoretical foundations of computer science. It focuses on Turing machines, introducing them as a simple mathematical model of a computer conceived by Alan Turing in 1936. Key aspects of a Turing machine are described, including its finite tape divided into cells, read/write head, state register, and finite table of instructions defining its behavior based on the current state and symbol read. Formal definitions of Turing machines and multi-tape Turing machines are also provided. The document aims to inform the reader about Turing machines and their role in modeling computational capability.
System calls
This presentation discusses system calls and provides an overview of their key aspects:
System calls provide an interface between processes and the operating system. They allow programs to request services from the OS like reading/writing files. There are different methods of passing parameters to the OS, such as via registers, parameter blocks, or pushing to the stack. System calls fall into categories including process control, file management, device management, information maintenance, and communication. An example is given of how system calls would be used in a program to copy data between two files.
Turing Machine
This presentation discusses Turing machines. It introduces Turing machines as a simple mathematical model of a computer that models computing capability. A Turing machine is represented as a 7-tuple that includes the finite set of states, input symbols, tape symbols, transition function, start state, blank symbol, and accepting states. The presentation covers various topics such as the Turing machine model, uses of Turing machines as language recognizers and generators, transition functions, instantaneous descriptions, variations of Turing machines, recursive and recursively enumerable languages, universal Turing machines, and properties of Turing machines.
Lecture: Automata
The document provides an introduction to automata theory and finite state automata (FSA). It defines an automaton as an abstract computing device or mathematical model used in computer science and computational linguistics. The reading discusses pioneers in automata theory like Alan Turing and his development of Turing machines. It then gives an overview of finite state automata, explaining concepts like states, transitions, alphabets, and using a example of building an FSA for a "sheeptalk" language to demonstrate these components.
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The document discusses MAC layer protocols, specifically CSMA/CD and CSMA/CA.
CSMA/CD is used for wired networks and works by having nodes listen to check if the medium is free before transmitting. If a collision is detected, transmission stops and resumes after a backoff time.
CSMA/CA is used for wireless networks and aims to avoid collisions through the use of request to send, clear to send, and acknowledgement frames exchanged between nodes, rather than detecting collisions.
Both protocols reduce collisions compared to simple CSMA, but CSMA/CA is less efficient and cannot completely solve collisions in wireless networks due to issues like hidden terminals.
pushdown automata
The document describes pushdown automata (PDA). A PDA has a tape, stack, finite control, and transition function. It accepts or rejects strings by reading symbols on the tape, pushing/popping symbols on the stack, and changing state according to the transition function. The transition function defines the possible moves of the PDA based on the current state, tape symbol, and stack symbol. If the PDA halts in a final state with an empty stack, the string is accepted. PDAs can recognize any context-free language. Examples are given of PDAs for specific languages.
Turing Machine
The document discusses Turing machines and their properties. It introduces the Church-Turing thesis that any problem that can be solved by an algorithm can be modeled by a Turing machine. It then describes different types of Turing machines, such as multi-track, nondeterministic, two-way, multi-tape, and multidimensional Turing machines. The document provides examples of Turing machines that accept specific languages and evaluate mathematical functions through their transition tables and diagrams.
Multi Head, Multi Tape Turing Machine
A multi-head Turing machine has a single tape with multiple heads that can read and write to the tape independently. Each head can move left, right, or stay in the same position. This type of Turing machine is as powerful as a standard single-tape Turing machine.
The halting problem asks if it is possible to determine if a Turing machine will halt or run infinitely given its program and input. It is proven to be unsolvable - there is no general algorithm that can correctly determine if all Turing machine programs will halt for all inputs.
Back propagation
The document discusses artificial neural networks and backpropagation. It provides an overview of backpropagation algorithms, including how they were developed over time, the basic methodology of propagating errors backwards, and typical network architectures. It also gives examples of applying backpropagation to problems like robotics, space robots, handwritten digit recognition, and face recognition.
Universal turing coastus
The document discusses Turing machines and languages. It introduces the concept of a universal Turing machine, which can simulate any other Turing machine. It then discusses countable and uncountable sets, proving that the set of all Turing machines and the set of rational numbers are countable, while the power set of any infinite countable set is uncountable. This implies that the set of all possible languages is uncountable, but the set of languages accepted by Turing machines is countable. Therefore, there must exist at least one language that is not accepted by any Turing machine.
Chomsky Normal Form
This document discusses the process of converting a context-free grammar (CFG) into Chomsky normal form (CNF) in multiple steps. It first recalls the definition of CNF and the theorem that every context-free language minus the empty string has a CFG in CNF. It then outlines the steps to convert a CFG to CNF: 1) remove epsilon productions, 2) remove unit rules, 3) break productions with more than two variables into chains of productions with two variables, and 4) ensure all productions are in the forms A->BC or A->a. The document provides two examples showing the full conversion process.
Turing machine by_deep
This document provides an overview of Turing machines. It introduces Turing machines as a simple mathematical model of a computer proposed by Alan Turing in 1936. A Turing machine consists of a tape divided into cells, a read/write head, finite states, and a transition function. The transition function defines how the machine moves between states and reads/writes symbols on the tape based on its current state and tape symbol. Turing machines can accept languages and compute functions. Variations include multi-tape, non-deterministic, multi-head, offline, multi-dimensional, and stationary-head Turing machines. The properties of Turing machines include their ability to recognize any language generated by a phrase-structure grammar and the Church
Water jug problem ai part 6
here is a presentation showing how problems are representated in Artificial intelligence.must watch my youtube videos on my channel techiseasy.
TOC 7 | CFG in Chomsky Normal Form
This Slide contains:
- CFG definition
- CNF definition
- CNF rules and violations
- CFG to CNF conversion
- Practice problems list
backpropagation in neural networks
Backpropagation is the algorithm that is used to train modern feed-forwards neural nets. This ppt aims to explain it succinctly.
Directory structure
- Directory structures organize files in a storage system and contain metadata about each file's name, location, size, and type. They allow operations like creating, searching, deleting, listing, and renaming files.
- Early systems used single-level directories with one list of all files, but this does not allow multiple files with the same name or grouping of files.
- Modern systems commonly use tree-structured directories that allow nesting files into subdirectories, making searching more efficient and allowing grouping of similar files. Directories can also be connected in acyclic graphs to enable sharing of files between directories through links.
Case based reasoning
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Np cooks theorem
The document discusses the theory of NP-completeness. It begins by defining the complexity classes P, NP, NP-hard, and NP-complete. It then explains the concepts of reduction and how none of the NP-complete problems can be solved in polynomial time deterministically. The document provides examples of NP-complete problems like satisfiability (SAT), vertex cover, and the traveling salesman problem. It shows how nondeterministic algorithms can solve these problems and how they can be transformed into SAT instances. Finally, it proves that SAT is the first NP-complete problem by showing it is in NP and NP-hard.
DBMS Unit - 3 - Relational query languages
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Similar to Automata presentation turing machine programming techniques
Word2vec and Friends
Word embeddings have received a lot of attention since some Tomas Mikolov published word2vec in 2013 and showed that the embeddings that the neural network learned by “reading” a large corpus of text preserved semantic relations between words. As a result, this type of embedding started being studied in more detail and applied to more serious NLP and IR tasks such as summarization, query expansion, etc… More recently, researchers and practitioners alike have come to appreciate the power of this type of approach and have started a cottage industry of modifying Mikolov’s original approach to many different areas.
In this talk we will cover the implementation and mathematical details underlying tools like word2vec and some of the applications word embeddings have found in various areas. Starting from an intuitive overview of the main concepts and algorithms underlying the neural network architecture used in word2vec we will proceed to discussing the implementation details of the word2vec reference implementation in tensorflow. Finally, we will provide a birds eye view of the emerging field of “2vec" (dna2vec, node2vec, etc...) methods that use variations of the word2vec neural network architecture.
This (long) version of the Tutorial was presented at #O'Reilly AI 2017 in San Francisco. See https://bmtgoncalves.github.io/word2vec-and-friends/ for further details.
Understanding the History of EncryptionUnderstanding the
Understanding the History of Encryption
Understanding the History of Encryption
Few facts -
Encryption has been around for a very long time. Instead of explaining the History of Cryptography, I am going to recommend a very good book by David Khan called "The Code Breakers" , published in 1996.
Instead of starting with early encryption algorithm, I am going to used two interesting encryption algorithms that will capture manu of the concepts that Khan outline in his book. The first algorithm is Character Substitution. Remember, the problem we are trying to solve is "how do we secure communications?". An example is - Suppose Alice and Bob wants to communicate securely using Eve is an attacker that wants to eavesdrop on the conversation. In order to communicate securely, Alice and Bob are going to share a secret key, which we are going to call K. Eve does not know anything about the key K.
Alice, Bob and even Eve know the algorithm (say character substitution). We all have played the substitution game in School. A is map to C and B is map to X and C is map to W and so on and so forth. So, if EVE gets the mapping and the ciphered text, she will be able to decipher the secret. Let supposed Eve gets the ciphered text but not the mapping (key). It will be difficult to figure the key out.
Assuming we have 26 letters, how many possibilities are there --> guess?
1. 26
2. 26X26
3. 26! (factorial)
4. 2^26
The answer is 26! which is about 2^88 if I remember correctly, which is a very large sample space. I hope you understand where I got the 26!. Think of one mapping and now think of another mapping and so on.
What is factorial? 1! = 1, 2! = 1X2 = 2, 3! = 1X2X3 = 6 and so on.
When I said 2^88, it is a good key since it is using 88 bits. Note that I am not saying it is great.
What I want you to do is to think about how you would break a substitution cipher? It is not hard but it needs some imagination.
The Substitution Cipher and How to break the Cipher
Breaking The Substitution Cipher
One way to break a substitution cipher is to use frequency analysis. Let's focus on the English language for now but the process works the same way for other languages. The character "e" occurs 12.7% in the Eglish language and the letter "t" occurs 9.35% and the letter "a" is 8.2% of the time. So, you can count each character in the ciphered text, compute its frequency and start replacing it. After you complete the first say 4 characters, you may want to change your strategy. Can you explain why you want to stop after the first 4 characters? The two-character "of" is about 4.16% and "to" is about 2%. Now try three characters frequency.
After a period of time, you will be able to decipher most of the text. I am going to ask you to do your first significant homework using frequency analysis.
One possibility for the substitution cipher:
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Y
Z
↕
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...
Turing Machine
The document discusses Turing machines, which can be both logical and physical devices. A Turing machine uses a tape like an infinite array and can read/write to cells on the tape and move left/right. It has a finite set of states and transition functions. Several examples are provided of designing Turing machines to perform tasks like reversing a binary number, checking for palindromes, and swapping all 'a's and 'b's in a string. In conclusion, Turing machines are an important theoretical model of computation that later inspired actual computer hardware.
Interview questions slide deck
The document provides guidance on how to succeed in coding interviews. It explains that employers use coding interviews to assess applicants' problem solving, algorithm design, and coding skills. It advises applicants to ask questions, think out loud, take time to design an approach before coding, test code thoroughly, and be prepared to discuss code efficiency. Examples of common coding interview questions and how to approach them are also provided. The document aims to help applicants feel comfortable with problem types and perform well in coding interviews.
Use Inductive or Deductive Logic to solve NP vs P?
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Word2vec in Theory Practice with TensorFlow
Neural networks for word embeddings have received a lot of attention since some Googlers published word2vec in 2013. They showed that the internal state (embeddings) that the neural network learned by "reading" a large corpus of text preserved semantic relations between words.
As a result, this type of embedding started being studied in more detail and applied to more serious Natural Language Processing + NLP and IR tasks such as summarization, query expansion, etc...
In this talk we will cover the intuitions and algorithms underlying word2vec family of algorithms. On the second half of the presentation we will quickly review than basics of tensorflow and analyze in detail the tensorflow reference implementation of word2vec
Artificial Intelligence – Case-based reasoning for recommender systems – Invi...
Artificial Intelligence – Case-based reasoning for recommender systems – Invi...Thomas Roth-Berghofer
Artificial Intelligence is mimicking cognitive abilities. Experience guides us in our learning efforts and is one of the most important assets for problem solving. Experience is everywhere. For example, a recording technician needs experience in the studio to produce a recording worth listening to. Does the recording sound full and rich or still too tinny? Does the bass section sound overwhelming? Experience — my own or someone else’s — can help me solve a current problem, for example, in the recording studio. Case-based reasoning, a methodology in which experience is expressed in the form cases, allows transferring and applying expert knowledge where needed.Thomas Roth-Berghofer (University of West London) – Artificial Intelligence -...
This document discusses case-based reasoning for recommender systems. Case-based reasoning is an approach to problem solving that involves storing past concrete experiences ("cases") and using them to solve new problems. The basic idea is to retrieve relevant past experiences ("cases") from a case base, reuse them to help solve a new problem, and store the new experience back in the case base. This allows the system to learn from experience. The document describes how case-based reasoning can be applied to build recommender systems that provide personalized recommendations by matching a user's requirements to similar past cases. It also introduces the myCBR tool that can be used to rapidly prototype case-based reasoning applications, including recommender systems.
9.6 Systems of Inequalities and Linear Programming
This document provides an overview of systems of inequalities and how to graph and solve them. It discusses representing systems of inequalities symbolically and identifying the solution as the overlapping region of the graphed inequalities. Examples are provided of writing systems of inequalities from word problems and using graphs to find the solutions. Linear programming is also introduced as an application of systems of inequalities to optimize an objective function subject to constraints.
Test Automation Day 2018
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[GAN by Hung-yi Lee]Part 1: General introduction of GAN
Generative Adversarial Network and its Applications on Speech and Natural Language Processing, Part 1.
발표자: Hung-yi Lee(국립 타이완대 교수)
발표일: 18.7.
Generative adversarial network (GAN) is a new idea for training models, in which a generator and a discriminator compete against each other to improve the generation quality. Recently, GAN has shown amazing results in image generation, and a large amount and a wide variety of new ideas, techniques, and applications have been developed based on it. Although there are only few successful cases, GAN has great potential to be applied to text and speech generations to overcome limitations in the conventional methods.
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Graph theory in Practise
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Using TypeScript at Dashlane
Using TypeScript at Dashlane
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Stanford splash spring 2016 basic programming
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Introduction to Transformers for NLP - Olga Petrova
Olga Petrova gives an introduction to transformers for natural language processing (NLP). She begins with an overview of representing words using tokenization, word embeddings, and one-hot encodings. Recurrent neural networks (RNNs) are discussed as they are important for modeling sequential data like text, but they struggle with long-term dependencies. Attention mechanisms were developed to address this by allowing the model to focus on relevant parts of the input. Transformers use self-attention and have achieved state-of-the-art results in many NLP tasks. Bidirectional Encoder Representations from Transformers (BERT) provides contextualized word embeddings trained on large corpora.
[GAN by Hung-yi Lee]Part 2: The application of GAN to speech and text processing
Generative Adversarial Network and its Applications on Speech and Natural Language Processing, Part 2.
발표자: Hung-yi Lee(국립 타이완대 교수)
발표일: 18.7.
Generative adversarial network (GAN) is a new idea for training models, in which a generator and a discriminator compete against each other to improve the generation quality. Recently, GAN has shown amazing results in image generation, and a large amount and a wide variety of new ideas, techniques, and applications have been developed based on it. Although there are only few successful cases, GAN has great potential to be applied to text and speech generations to overcome limitations in the conventional methods.
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constructing_generic_algorithms__ben_deane__cppcon_2020.pdf
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02 naive bays classifier and sentiment analysis
Naive Bayes classifiers are a simple yet effective method for sentiment analysis and text classification problems. They work by calculating the probability of a document belonging to a certain class based on the presence of individual words or features, assuming conditional independence between features given the class. This allows probabilities to be estimated efficiently from training data. While the independence assumption is often unrealistic, naive Bayes classifiers generally perform well compared to more sophisticated approaches. The document discusses various techniques for preprocessing text like tokenization, stemming, part-of-speech tagging, and negation handling to improve the accuracy of naive Bayes classifiers for sentiment analysis tasks.
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Wendell Kuling works as a Data Scientist at ING in the Wholesale Banking Advanced Analytics team. Their projects aim to provide better services to corporate customers of ING, by using innovative techniques from data-science. In this talk, Wendell covers key insights from their experience in matching large datasets based on names. After covering the key algorithms and packages ING uses for name matching, Wendell will share his best-practice approach in applying these algorithms at scale… would you bet on a Cruncher (48-CPU/512 MB RAM machine), a Tesla (Cuda Tesla K80 with 4992 cores, 24GB memory) or a Spark cluster (80 cores/2,5 TB memory)?
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Understanding the History of EncryptionUnderstanding the
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Use Inductive or Deductive Logic to solve NP vs P?
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[GAN by Hung-yi Lee]Part 1: General introduction of GAN
[GAN by Hung-yi Lee]Part 1: General introduction of GAN
Introduction to Transformers for NLP - Olga Petrova
Introduction to Transformers for NLP - Olga Petrova
[GAN by Hung-yi Lee]Part 2: The application of GAN to speech and text processing
[GAN by Hung-yi Lee]Part 2: The application of GAN to speech and text processing
constructing_generic_algorithms__ben_deane__cppcon_2020.pdf
constructing_generic_algorithms__ben_deane__cppcon_2020.pdf
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
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Automata presentation turing machine programming techniques
- 2. Group Member Basit Hussain 2017 – CS- 126 Hamza 2017 – CS- 105 Irtiza 2017 – CS- 126 Minhaj Wajid 2017 – CS- 051 Muhammad Umer Khan 2017 – CS- 027
- 3. Tu r i n g M a c h i n e P r o g r a m m i n g Te c h n i q u e s Problem: How can we recognize the left end of the tape of a Turing machine? Solution: Put a Special Symbol $ on the left end of the Tape and shift the input over one cell to the right a a a b b a …. a a a b b a ….$
- 4. Tu r i n g M a c h i n e P r o g r a m m i n g Te c h n i q u e s over one cell to the right. a a a b b a …. a a a b b a ….$ D B C A a $, R a a, R a, L $ $, R b $, R a b, R b a, R a a, L b b, L b, L b b, R
- 5. Tu r i n g M a c h i n e P r o g r a m m i n g Te c h n i q u e s Example: Building a Turing Machine to recognize the language 0N 1N 0N We already hava a Turing Machine to turn 0N 1N to XN YN and to decide that language. USE THIS TURING MACHINE AS A SUBROUTINE IDEA
- 6. Tu r i n g M a c h i n e P r o g r a m m i n g Te c h n i q u e s Example: Building a Turing Machine to recognize the language 0N 1N 0N Step 1: 0 0 0 0 1 1 1 1 0 0 0 0 x x x x y y y y 0 0 0 0 Step 2: Build a similar Turing Machine to recognize yN 0N Step 3: Build a final Turing Machine combining these two smaller Turinng Machine together into one larger Turing Machine
- 7. Tu r i n g M a c h i n e P r o g r a m m i n g Te c h n i q u e s Comparing Two Strings A Turing Machine to decide { w # w | w E {a, b, c}*} Solution: • Use a new Symbol such as ‘x’. • Replace each Symbol into an x after it has been examined.
- 8. Tu r i n g M a c h i n e P r o g r a m m i n g Te c h n i q u e s Solution: • Use a new Symbol such as ‘x’. • Replace each Symbol into an x after it has been examined. a b b a c # a b b a c x b b a c # a b b a c x x b a c # a b b a c x x x c # a b b a c a b x a c # a b b a c a b b a c # a b b a c We lost the original String
- 9. Tu r i n g M a c h i n e P r o g r a m m i n g Te c h n i q u e s Problem: Can we do its non-destructively? i.e without losing the original strings? Solution: Replace each unique symbol with another unique symbol instead of replacing all with the same symbol. a b b a c # a b b a c p q q p r # p q q p r Restore the original string if required