- Introducción
- Computadora electrónica
- Lógica booleana
- Sistemas numéricos
- Unidad Central de Procesamiento (CPU)
- Registros, caché y RAM
- Instrucciones y programa
- Lenguajes de programación
- Tipos datos
- Declaraciones y Funciones
- Estructuras de datos
- Algoritmos
- Alan Turing
- Ingeniería de software
- Circuitos integrados
- Programación orientada a objetos
- Programación funcional
- Sistemas operativos
- Memoria y almacenamiento
- Sistema de archivos
- Computación en la nube
- Aprendizaje automático
- Tecnología Web
- Redes
- Internet
- DBMS (Sistema de gestión de bases de datos)
- Criptografía
- Teoría de la Computación
Computer science is the study of computers and computing and their theoretical and practical applications. Computer science applies the principles of mathematics, engineering, and logic to a plethora of problems. These include algorithm formulation, software/hardware development, and artificial intelligence.
A device that computes, especially a programmable electronic machine that performs high-speed mathematical or logical operations or assembles, stores, correlates, or otherwise processes information.
Boolean logic is a branch of mathematics that deals with the values of truth and falsehood. It is a system of logic that uses only two values, 0 and 1, to represent false and true, respectively. It is also known as Boolean algebra, named after George Boole, who first described it in 1854.
| Operator | Name | Description |
|---|---|---|
| ! | NOT | Negates the value of the operand. |
| && | AND | Returns true if both operands are true. |
| || | OR | Returns true if one or all operands are true. |
| Operator | Name | Description |
|---|---|---|
| () | Parentheses | Allows you to group keywords and control the order in which the terms will be searched. |
| “” | Quotation marks | Provides results with the exact phrase. |
| * | Asterisk | Provides results containing a keyword variation. |
| ⊕ | XOR | Returns true if the operands are different |
| ⊽ | NOR | Returns true if all operands are false. |
| ⊼ | NAND | Returns false only if both values of its two inputs are true. |
Number systems are mathematical systems for expressing numbers. A number system consists of a set of symbols that are used to represent numbers and a set of rules for manipulating those symbols. The symbols used in a number system are called numerals.
Binary is a base-2 number system invented by Gottfried Leibniz that's made up of only two numbers or digits: 0 (zero) and 1 (one). This numbering system is the basis for all binary code, which is used to write digital data such as the computer processor instructions used every day. The 0s and 1s in binary represent OFF or ON, respectively. In a transistor, a "0" represents no flow of electricity, and a "1" represents electricity is allowed to flow. In this way, numbers are represented physically inside the computing device, permitting calculations.
Binary is still the primary language for computers and is used with electronics and computer hardware for the following reasons:
- It is a simple and elegant design.
- Binary's 0 and 1 method is quick to detect an electrical signal's off (false) or on (true) state.
- Having only two states placed far apart in an electrical signal makes it less susceptible to electrical interference.
- The positive and negative poles of magnetic media are quickly translated to binary.
- Binary is the most efficient way to control logic circuits.
A Central Processing Unit (CPU) is the most important part of any computer. The CPU sends signals to control the other parts of the computer, almost like how a brain controls a body. The CPU is an electronic machine that works on a list of computer things to do, called instructions. It reads the list of instructions and runs (executes) each one in order. A list of instructions that a CPU can run is a computer program. A CPU can process more than one instruction at a time on sections called "cores". A CPU with four cores may process four programs at once. The CPU itself is made of three main components. They are:
Registers are small amounts of high-speed memory contained within the CPU. Registers are a collection of "flip-flops" (a circuit used to store 1 bit of memory). They are used by the processor to store small amounts of data that are needed during processing. A CPU may have several sets of registers that are called "cores". Register also helps in arithmetic and logic operations.
Arithmetic operations are mathematical calculations performed by the CPU on numerical data stored in registers. These operations include addition, subtraction, multiplication, and division. Logic operations are Boolean calculations performed by the CPU on binary data stored in registers. These operations include comparisons (e.g. testing if two values are equal) and logical operations (e.g. AND, OR, NOT).
Registers are essential for performing these operations because they allow the CPU to quickly access and manipulate small amounts of data. By storing frequently accessed data in registers, the CPU can avoid the slower process of retrieving data from memory.
Larger amounts of data may be stored in Cache (pronounced as "cash"), a very fast memory located on the same integrated circuit as the registers. Cache is used for data frequently accessed as the program runs. Even larger amounts of data may be stored in RAM. RAM stands for random-access memory, which is a type of memory that holds data and instructions that have been moved from disk storage until the processor needs it.
Cache memory is a chip-based computer component that makes retrieving data from the computer's memory more efficient. It acts as a temporary storage area so the computer's processor can retrieve data easily. This temporary storage area, known as a cache, is more readily available to the processor than the computer's main memory source, typically some form of DRAM.
Cache memory is sometimes called CPU (central processing unit) memory because it is typically integrated directly into the CPU chip or placed on a separate chip that has a separate bus interconnect with the CPU. Therefore, it is more accessible to the processor and able to increase efficiency because it's physically close to the processor.
To be close to the processor, cache memory needs to be much smaller than the main memory. Consequently, it has less storage space. It is also more expensive than the main memory, as it is a more complex chip that yields higher performance.
What it sacrifices in size and price, it makes up for in speed. Cache memory operates 10 to 100 times faster than RAM, requiring only a few nanoseconds to respond to a CPU request.
The name of the actual hardware that is used for cache memory is high-speed static random access memory (SRAM). The name of the hardware that is used in a computer's main memory is dynamic random-access memory (DRAM).
Cache memory is not to be confused with the broader term cache. Caches are temporary data stores that can exist in both hardware and software. Cache memory refers to the specific hardware component that allows computers to create caches at various levels of the network. A cache is a hardware or software that is used to store something, typically data, temporarily in a computing environment.
RAM (Random Access Memory) is a form of computer memory that can be read and changed in any order, typically used to store working data and machine code. A random access memory device allows data items to be read or written in almost the same amount of time regardless of the physical location of data inside the memory, in contrast with other direct-access data storage media (such as hard disks, CD-RWS, DVD-RWs and the older magnetic tapes and drum memory), where the time required to read and write data items varies significantly depending on their physical locations on the recording medium, due to mechanical limitations such as media rotation speeds and arm movement.
In computer science, an instruction is a single operation of a processor defined by the processor instruction set. A computer program is a list of instructions that tell a computer what to do. Everything a computer does is accomplished by using a computer program. Programs that are stored in the memory of a computer ("internal programming") let the computer do one thing after another, even with breaks in between.
A programming language is any set of rules that convert strings, or graphical program elements in the case of visual programming languages, to various kinds of machine code output. Programming languages are one kind of computer language used in computer programming to implement algorithms.
Programming languages are often divided into two broad categories:
- High-level language uses a syntax similar to the English language. The source code is converted into machine-understandable machine code using a compiler or an interpreter. Java and Python are some examples of high-level programming languages. These are usually slower than Low-level, but it comes with being easier.
- Low-level programming languages work more closely with the hardware and have more control over it. They directly interact with the hardware. Two common examples of low-level languages are machine language and assembly language. These are usually faster than High-level, but it comes at the cost of very great difficulty and lack of readability.
There are also several different programming paradigms. Programming paradigms are different ways or styles in which a given program or programming language can be organized. Each paradigm consists of certain structures, features, and opinions about how common programming problems should be addressed.
Programming paradigms are not languages or tools. You can't "build" anything with a paradigm. They are more like a set of ideals and guidelines that many people have agreed on, followed, and expanded upon. Programming languages aren't always tied to a particular paradigm. There are languages that have been built with a certain paradigm in mind and have features that facilitate that kind of programming more than others (Haskell and functional programming is a good example). But there are also "multi-paradigm" languages in which you can adapt your code to fit a certain paradigm or other (JavaScript and Python are good examples).
A data type, in programming, is a classification that specifies which type of value a variable has and what type of mathematical, relational, or logical operations can be applied to it without causing an error.
Primitive data types are the most basic data types in a programming language. They are the building blocks of more complex data types. Primitive data types are predefined by the programming language and are named by a reserved keyword.
Non-primitive data types are also known as reference data types. They are created by the programmer and are not defined by the programming language. Non-primitive data types are also called composite data types because they are composed of other types.
In computer programming, a statement is a syntactic unit of an imperative programming language that expresses some action to be carried out. A program written in such a language is formed by a sequence of one or more statements. A statement may have internal components (e.g., expressions). There are two main types of statements in any programming language that is necessary to build the logic of a code.
There are two types of conditional statements mainly:
- if
- if-else
- switch case
There are three types of conditional statements mainly:
- for loop
- while loop
- do - while loop (a variation of while loop)
- do - Until loop
A function is a block of statements that performs a specific task. Functions accept data, process it, and return a result or execute it. Functions are written primarily to support the concept of reusability. Once a function is written, it can be called easily without having to repeat the same code.
Different functional languages use different syntaxes to write functions.
Read more about functions here
In computer science, a data structure is a data organization, management, and storage format that enables efficient access and modification. More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data.
Algorithms are the sets of steps necessary to complete computation. They are at the heart of what our devices do, and this isn’t a new concept. Since the development of math itself, algorithms have been needed to help us complete tasks more efficiently, but today we’re going to take a look at a couple of modern computing problems like sorting and graph search and show how we’ve made them more efficient so you can more easily find cheap airfare or map directions to Winterfell or a restaurant or something.
The time complexity of an algorithm estimates how much time the algorithm will use for some input. The idea is to represent efficiency as a function whose parameter is the input size. By calculating the time complexity, we can determine whether the algorithm is fast enough without implementing it.
Space complexity refers to the total amount of memory space an algorithm/program uses, including the space of input values for execution. Calculate the space occupied by variables in an algorithm/program to determine space complexity.
Sorting is the process of arranging a list of items in a particular order. For example, if you had a list of names, you might want to sort them alphabetically. Alternatively, if you had a list of numbers, you might want to put them in order from smallest to largest. Sorting is a common task, and it’s one that we can do in many different ways.
Searching is an algorithm for finding a certain target element inside a container. Searching Algorithms are designed to check for an element or retrieve an element from any data structure where it is stored.
Graph search is the process of searching through a graph to find a particular node. A graph is a data structure that consists of a finite (and possibly mutable) set of vertices or nodes or points, together with a set of unordered pairs of these vertices for an undirected graph or a set of ordered pairs for a directed graph. These pairs are known as edges, arcs, or lines for an undirected graph, and as arrows, directed edges, directed arcs, or directed lines for a directed graph. The vertices may be part of the graph structure or may be external entities represented by integer indices or references. Graphs are one of the most useful data structures for many real-world applications. Graphs are used to model pairwise relations between objects. For example, the airline route network is a graph in which the cities are the vertices, and the flight routes are the edges. Graphs are also used to represent networks. The Internet can be modeled as a graph in which the computers are the vertices, and the links between computers are the edges. Graphs are also used on social networks like LinkedIn and Facebook. Graphs are used to represent many real-world applications: computer networks, circuit design, and aeronautical scheduling to name just a few.
Dynamic programming is both a mathematical optimization method and a computer programming method. Richard Bellman developed the method in the 1950s and has found applications in numerous fields, from aerospace engineering to economics. In both contexts, it refers to simplifying a complicated problem by breaking it down into simpler sub-problems in a recursive manner. While some decision problems cannot be taken apart this way, decisions that span several points in time do often break apart recursively. Likewise, in computer science, if a problem can be solved optimally by breaking it into sub-problems and then recursively finding the optimal solutions to the sub-problems, then it is said to have an optimal substructure. Dynamic programming is one way to solve problems with these properties. The process of breaking a complicated problem down into simpler sub-problems is called "divide and conquer".
Greedy algorithms are a simple, intuitive class of algorithms that can be used to find the optimal solution to some optimization problems. They are called greedy because, at each step, they make the choice that seems best at that moment. This means that greedy algorithms do not guarantee to return the globally optimal solution but instead make locally optimal choices in the hope of finding a global optimum. Greedy algorithms are used for optimization problems. An optimization problem can be solved using Greedy if the problem has the following property: at every step, we can make a choice that looks best at the moment, and we get the optimal solution to the complete problem.
Backtracking is an algorithmic technique for solving problems recursively by trying to build a solution incrementally, one piece at a time, removing those solutions that fail to satisfy the constraints of the problem at any point in time (by time, here, is referred to the time elapsed till reaching any level of the search tree).
Branch and bound is a general technique for solving combinatorial optimization problems. It is a systematic enumeration technique that reduces the number of candidate solutions by using the problem's structure to eliminate candidate solutions that cannot possibly be optimal.
Time Complexity: It is defined as the number of times a particular instruction set is expected to be executed rather than the total time taken. Since time is a dependent phenomenon, time complexity may vary on some external factors like processor speed, the compiler used, etc.
Space Complexity: It is the total memory space consumed by the program for its execution.
Both are calculated as the function of input size(n). The time complexity of an algorithm is expressed in big O notation.
The efficiency of an algorithm depends on these two parameters.
Types Of Time Complexity :
- Best Time Complexity: The input for which the algorithm takes less time or minimum time. In the best case, we calculate the lower bound time complexity of an algorithm. For example: if the data to be searched is present at the first location of a large data array in a linear search, then the best case occurs.
- Average Time Complexity: We take all random inputs and calculate the computation time for all inputs. And then, we divide it by the total number of inputs.
- Worst Time Complexity: Define the input for which algorithm takes a long time or maximum time. In the worst case, we calculate the upper bound of an algorithm. Example: If the data to be searched is present at the last location of a large data array in a linear search algorithm, then the worst case occurs.
Some common time complexities are :
-
O(1): This denotes the constant time. O(1) usually means that an algorithm will have a constant time regardless of the input size. Hash Maps are perfect examples of constant time.
-
O(log n): This denotes logarithmic time. O(log n) means to decrease with each instance for the operations. Searching for elements in Binary Search Trees (BSTs) is a good example of logarithmic time.
-
O(n): This denotes linear time. O(n) means that the performance is directly proportional to the size of the input. In simple terms, the number of inputs and the time taken to execute those inputs will be proportional. Linear search in arrays is a great example of linear time complexity.
-
O(n*n): This denotes quadratic time. O(n^2) means that the performance is directly proportional to the square of the input taken. In simple, the time taken for execution will roughly take square times the input size. Nested loops are perfect examples of quadratic time complexity.
-
O(n log n): This denotes polynomial time complexity. O(n log n) means that the performance is n times that of O(log n), (which is the worst-case complexity). A good example would be divided and conquer algorithms such as merge sort. This algorithm first divides the set, which takes O(log n) time, then conquers and sorts through the set, which takes O(n) time- therefore, Merge sort takes O(n log n) time.
| Algorithm | Time Complexity | Space Complexity | ||
|---|---|---|---|---|
| Best | Average | Worst | Worst | |
| Selection Sort | Ω(n^2) | θ(n^2) | O(n^2) | O(1) |
| Bubble Sort | Ω(n) | θ(n^2) | O(n^2) | O(1) |
| Insertion Sort | Ω(n) | θ(n^2) | O(n^2) | O(1) |
| Heap Sort | Ω(n log(n)) | θ(n log(n)) | O(n log(n)) | O(1) |
| Quick Sort | Ω(n log(n)) | θ(n log(n)) | O(n^2) | O(n) |
| Merge Sort | Ω(n log(n)) | θ(n log(n)) | O(n log(n)) | O(n) |
| Bucket Sort | Ω(n +k) | θ(n +k) | O(n^2) | O(n) |
| Radix Sort | Ω(nk) | θ(nk) | O(nk) | O(n + k) |
| Count Sort | Ω(n +k) | θ(n +k) | O(n +k) | O(k) |
| Shell Sort | Ω(n log(n)) | θ(n log(n)) | O(n^2) | O(1) |
| Tim Sort | Ω(n) | θ(n log(n)) | O(n log(n)) | O(n) |
| Tree Sort | Ω(n log(n)) | θ(n log(n)) | O(n^2) | O(n) |
| Cube Sort | Ω(n) | θ(n log(n)) | O(n log(n)) | O(n) |
| Algorithm | Time Complexity | ||
|---|---|---|---|
| Best | Average | Worst | |
| Linear Search | O(1) | O(N) | O(N) |
| Binary Search | O(1) | O(logN) | O(logN) |
Alan Turing (born June 23, 1912, London, Eng.—died June 7, 1954, Wilmslow, Cheshire) was an English mathematician and logician. He studied at the University of Cambridge and Princeton's Institute for Advanced Study. In his seminal 1936 paper "On Computable Numbers," he proved that there could not exist any universal algorithmic method of determining the truth in mathematics and that mathematics will always contain undecidable (as opposed to unknown) propositions. That paper also introduced the Turing machine. He believed that computers eventually would be capable of thought indistinguishable from that of a human and proposed a simple test (see Turing test) to assess this capability. His papers on the subject are widely acknowledged as the foundation of research in artificial intelligence. He did valuable work in cryptography during World War II, playing an important role in breaking the Enigma code used by Germany for radio communications. After the war, he taught at the University of Manchester and began work on what is now known as artificial intelligence. Amid this groundbreaking work, Turing was found dead in his bed, poisoned by cyanide. His death followed his arrest for a homosexual act (then a crime) and sentence of 12 months of hormone therapy.
Following a public campaign in 2009, British Prime Minister Gordon Brown made an official public apology on behalf of the British government for the appalling way Turing was treated. Queen Elizabeth II granted a posthumous pardon in 2013. The term "Alan Turing law" is now used informally to refer to a 2017 law in the United Kingdom that retroactively pardoned men cautioned or convicted under historical legislation that outlawed homosexual acts.
Turing has an extensive legacy with statues of him and many things named after him, including an annual award for computer science innovations. He appears on the current Bank of England £50 note, which was released on June 23, 2021, to coincide with his birthday. A 2019 BBC series, as voted by the audience, named him the greatest person of the 20th century.
Software engineering is the branch of computer science that deals with the design, development, testing, and maintenance of software applications. Software engineers apply engineering principles and knowledge of programming languages to build software solutions for end users.
Let's look at the various definitions of software engineering:
- IEEE, in its standard 610.12-1990, defines software engineering as the application of a systematic, disciplined, which is a computable approach for the development, operation, and maintenance of software.
- Fritz Bauer defined it as 'the establishment and used standard engineering principles. It helps you to obtain economical software that is reliable and works efficiently on real machines.
- Boehm defines software engineering as involving 'the practical application of scientific knowledge to the creative design and building of computer programs. It also includes associated documentation needed for developing, operating, and maintaining them.'
Successful engineers know how to use the right programming languages, platforms, and architectures to develop everything from computer games to network control systems. In addition to building their systems, software engineers also test, improve, and maintain software built by other engineers.
In this role, your day-to-day tasks might include the following:
- Designing and maintaining software systems
- Evaluating and testing new software programs
- Optimizing software for speed and scalability
- Writing and testing code
- Consulting with clients, engineers, security specialists, and other stakeholders
- Presenting new features to stakeholders and internal customers
The software engineering process involves several phases, including requirements gathering, design, implementation, testing, and maintenance. By following a disciplined approach to software development, software engineers can create high-quality software that meets the needs of its users.
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The first phase of software engineering is requirements gathering. In this phase, the software engineer works with the client to determine the software's functional and non-functional requirements. Functional requirements describe what the software should do, while non-functional requirements describe how well it should do it. Requirements gathering is a critical phase, as it lays the foundation for the entire software development process.
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After the requirements are gathered, the next phase is design. In this phase, the software engineer creates a detailed plan for the software's architecture and functionality. This plan includes a software design document that specifies the software's structure, behavior, and interactions with other systems. The software design document is essential as it serves as a blueprint for the implementation phase.
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The implementation phase is where the software engineer creates the actual code for the software. This is where the design document is transformed into working software. The implementation phase involves writing code, compiling it, and testing it to ensure that it meets the requirements specified in the design document.
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Testing is a critical phase in software engineering. In this phase, the software engineer checks to ensure that the software functions correctly, is reliable and is easy to use. This involves several types of testing, including unit testing, integration testing, and system testing. Testing ensures that the software meets the requirements and functions as expected.
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The final phase of software engineering is maintenance. In this phase, the software engineer makes changes to the software to correct errors, add new features, or improve its performance. Maintenance is an ongoing process that continues throughout the software's lifetime.
- Computer Science: Gives the scientific foundation for the software as electrical engineering mainly depends on physics.
- Management Science: Software engineering is labor-intensive and demands technical and managerial control. Therefore, it is widely used in management science.
- Economics: In this sector, software engineering helps you estimate resources and control costs. A computing system must be developed, and data should be maintained regularly within a given budget.
- System Engineering: Most software is a component of a much larger system. For example, the software in an Industry monitoring system or the flight software on an airplane. Software engineering methods should be applied to the study of this type of system.
An integrated circuit or monolithic integrated circuit (also referred to as an IC, a chip, or a microchip) is a set of electronic circuits on one small flat piece (or "chip") of semiconductor material, usually silicon. Many tiny MOSFETs (metal–oxide–semiconductor field-effect transistors) integrate into a small chip. This results in circuits that are orders of magnitude smaller, faster, and less expensive than those constructed of discrete electronic components. The IC's mass production capability, reliability, and building-block approach to integrated circuit design have ensured the rapid adoption of standardized ICs in place of discrete transistors. ICs are now used in virtually all electronic equipment and have revolutionized the world of electronics. Computers, mobile phones, and other home appliances are now inextricable parts of the structure of modern societies, made possible by the small size and low cost of ICs such as modern computer processors and microcontrollers.
Very-large-scale integration was made practical by technological advancements in metal–oxide–silicon (MOS) semiconductor device fabrication. Since their origins in the 1960s, the size, speed, and capacity of chips have progressed enormously, driven by technical advances that fit more and more MOS transistors on chips of the same size – a modern chip may have many billions of MOS transistors in an area the size of a human fingernail. These advances, roughly following Moore's law, make today's computer chips possess millions of times the capacity and thousands of times the speed of the computer chips of the early 1970s.
ICs have two main advantages over discrete circuits: cost and performance. The cost is low because the chips, with all their components, are printed as a unit by photolithography rather than being constructed one transistor at a time. Furthermore, packaged ICs use much less material than discrete circuits. Performance is high because the IC's components switch quickly and consume comparatively little power because of their small size and proximity. The main disadvantage of ICs is the high cost of designing them and fabricating the required photomasks. This high initial cost means ICs are only commercially viable when high production volumes are anticipated.
Modern electronic component distributors often further sub-categorize integrated circuits:
- Digital ICs are categorized as logic ICs (such as microprocessors and microcontrollers), memory chips (such as MOS memory and floating-gate memory), interface ICs (level shifters, serializer/deserializer, etc.), power management ICs, and programmable devices.
- Analog ICs are categorized as linear integrated circuits and RF circuits (radio frequency circuits).
- Mixed-signal integrated circuits are categorized as data acquisition ICs (A/D converters, D/A converters, and digital potentiometers), clock/timing ICs, switched capacitor (SC) circuits, and RF CMOS circuits.
- Three-dimensional integrated circuits (3D ICs) are categorized into through-silicon via (TSV) ICs and Cu-Cu connection ICs.
Object Oriented Programming is a fundamental programming paradigm that is based on the concepts of objects and data.
It is the standard way of code that every programmer has to abide by for better readability and reusability of the code.
- Abstraction
- Encapsulation
- Inheritance
- Polymorphism
Read more about these concepts of OOP here
In computer science, functional programming is a programming paradigm where programs are constructed by applying and composing functions. It is a declarative programming paradigm in which function definitions are trees of expressions that map values to other values, rather than a sequence of imperative statements which update the running state of the program.
In functional programming, functions are treated as first-class citizens, meaning that they can be bound to names (including local identifiers), passed as arguments, and returned from other functions, just as any other data type can. This allows programs to be written in a declarative and composable style, where small functions are combined in a modular manner.
Functional programming is sometimes treated as synonymous with purely functional programming, a subset of functional programming which treats all functions as deterministic mathematical functions, or pure functions. When a pure function is called with some given arguments, it will always return the same result, and cannot be affected by any mutable state or other side effects. This is in contrast with impure procedures, common in imperative programming, which can have side effects (such as modifying the program's state or taking input from a user). Proponents of purely functional programming claim that by restricting side effects, programs can have fewer bugs, be easier to debug and test, and be more suited to formal verification procedures.
Functional programming has its roots in academia, evolving from the lambda calculus, a formal system of computation based only on functions. Functional programming has historically been less popular than imperative programming, but many functional languages are seeing use today in industry and education.
Some example of functional programming languages are:
Functional programming is derived historically from the lambda calculus. Lambda calculus is a framework developed by Alonzo Church to study computations with functions. It is often called "the smallest programming language in the world." It provides a defintion of what is computable and what is not. It is equivalaent to a Turing machine in its computational ability and anything computable by the lambda calculus, just like anything computable by a Turing machine, is computable. It provides a theoretical frammework for describing functions and their evaluations.
Some essential concepts of functional programming are:
- Pure functions
- Recursion
- Referential transparancy
- Functions as first class and higher order functions
- Variable are immutable.
Pure functions: These functions have two main properties. First, they always produce the same output for the same arguments irrespective of anything else. Secondly, they have no side-effects. i.e. they do not modify any arguments or local/global variable or input/output streams. The latter property is called immutability. The pure function's only result is the value it returns. They are deterministic. Programs done using functional programming are easy to debug because they have no side-effects or hidden I/O. Pure functions alsoì make it easier to write parralel/concurrent applications. When code is written in this style, a smart compiler can do many things- it can parallelize the instructions, wait to evaluate results until needed and memorize the results since the results never change as long as the input doesn't change. Here is a simple example of a pure funtion in Python:
def sum(x ,y): # sum is a function taking x and y as arguments
return x + y # returns x + y without changing the valueRecursion: There are no "for" or "while" loops in pure functional programming languages. Iteration is implemented through recursion. Recursive functions repeatedly call themselves until a base case is reached. Here is a simple example of a recursice function in C:
int fib(n) {
if(n <= 1)
return 1;
else
return (fib(n-1) + fib(n-2));
}Referential transparency: In functional programs, variables once defined do not change their value throughout the program. Functional programs do not have assignment statements. If we have to store some value, we define a new variable instead. This elimimates any chance of side-effects because any variable can be replaced with its actual value at any point of the execution. The state of any varaible is constant at any instant. Example:
x = x + 1 # this changed the value assigned to the varable x
# therefore, the expression is NOT referentially transparentFunctions are first-class and can be higher order: First class functions are treated as first-class variables. The first class variables can be passed to functions as parameters, can be returned from functions or stored in data structures.
A combination of function applications may be defined using a LISP form called funcall, which takes as arguments a function and a series of arguments and applies that function to those arguments:
(defun filter (list-of-elements test)
(cond ((null list-of-elements) nil)
((funcall test (car list-of-elements))
(cons (car list-of-elements)
(filter (cdr list-of-elements)
test)))
(t (filter (cdr list-of-elements)
test))))The function filter applies the test to the first element of the list. If the test returns non-nil, it conses the element onto the resul of filter applied to the cdr of the list; otherwise, it just returns the filtered cdr. This function may be used with different predicates passed in as parameters to perform a variety of filtering tasks:
> (filter '(1 3 -9 5 -2 -7 6) #'plusp) ; filter out all negative numbers output: (1 3 5 6)
> (filter '(1 2 3 4 5 6 7 8 9) #'evenp) ; filter out all odd numbersoutput: (2 4 6 8)
and so on.
Variables are immutable: In functional programming, we can't modify a variable after it's beem initialized. We can create new variables- but we can't modify existing variable, and this really helps to maintain state throughout the runtime of a program. Once we create a variable ans set its value, we can have full confidence knowing that the value of that variable will never change.
An operating system (or OS for short) acts as an intermediary between a computer user and computer hardware. The purpose of an operating system is to provide an environment in which a user can execute programs conveniently and efficiently. An operating system is software that manages computer hardware. The hardware must provide appropriate mechanisms to ensure the correct operation of the computer system and to prevent user programs from interfering with the proper operation of the system. An even more common definition is that the operating system is the one program running at all times on the computer (usually called the kernel), with all else being application programs.
Operating systems can be viewed from two viewpoints: resource managers and extended machines. In the resource-manager view, the operating system's job is to manage the different parts of the system efficiently. In the extended-machine view, the job of the system is to provide the users with abstractions that are more con- convenient to use than the actual machine. These include processes, address spaces, and files. Operating systems have a long history, from when they replaced the operator to modern multiprogramming systems. Highlights include early batch systems, multiprogramming systems, and personal computer systems. Since operating systems interact closely with the hardware, some knowledge of computer hardware is useful for understanding them. Computers are built up of processors, memories, and I/O devices. These parts are connected by buses. The basic concepts on which all operating systems are built are processes, memory management, I/O management, the file system, and security. The heart of any operating system is the set of system calls that it can handle. These tell what the operating system does.
The operating system manages all the pieces of a complex system. Modern computers consist of processors, memories, timers, disks, mice, network interfaces, printers, and a wide variety of other devices. In the bottom-up view, the job of the operating system is to provide for an orderly and controlled allocation of the processors, memories, and I/O devices among the various programs wanting them. Modern operating systems allow multiple programs to be in memory and run simultaneously. Imagine what would happen if three programs running on some computer all tried to print their output simultaneously on the same printer. The result would be utter chaos. The operating system can bring order to the potential chaos by buffering all the output destined for the printer on the disk. When one program is finished, the operating system can then copy its output from the disk file where it has been stored for the printer, while at the same time, the other program can continue generating more output, oblivious to the fact that the output is not going to the printer (yet). When a computer (or network) has more than one user, the need to manage and protect the memory, I/O devices, and other resources even more since the users might otherwise interfere with one another. In addition, users often need to share not only hardware but also information (files, databases, etc.). In short, this view of the operating system holds that its primary task is to keep track of which programs are using which resource, to grant resource requests, to account for usage and to mediate conflicting requests from different programs and users.
The architecture of most computers at the machine-language level is primitive and awkward to program, especially for input/output. To make this point more concrete, consider modern SATA (Serial ATA) hard disks used on most computers. What a programmer would have to know to use the disk. Since then, the interface has been revised multiple times and is more complicated than it was in 2007. No sane programmer would want to deal with this disk at the hardware level. Instead, a piece of software called a disk driver deals with the hardware and provides an interface to read and write disk blocks, without getting into the details. Operating systems contain many drivers for controlling I/O devices. But even this level is much too low for most applications. For this reason, all operating systems provide yet another layer of abstraction for using disks: files. Using this abstraction, programs can create, write, and read files without dealing with the messy details of how the hardware works. This abstraction is the key to managing all this complexity. Good abstractions turn a nearly impossible task into two manageable ones. The first is defining and implementing the abstractions. The second is using these abstractions to solve the problem at hand.
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First Generation (1945-55): Little progress was achieved in building digital computers after Babbage's disastrous efforts until the World War II era. At Iowa State University, Professor John Atanasoff and his graduate student Clifford Berry created what is today recognized as the first operational digital computer. Konrad Zuse in Berlin constructed the Z3 computer using electromechanical relays around the same time. The Mark I was created by Howard Aiken at Harvard, the Colossus by a team of scientists at Bletchley Park in England, and the ENIAC by William Mauchley and his doctoral student J. Presper Eckert at the University of Pennsylvania in 1944.
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Second Generation (1955-65): The transistor's invention in the middle of the 1950s drastically altered the situation. Computers became dependable enough that they could be manufactured and sold to paying customers with the assumption that they would keep working long enough to conduct some meaningful job. Mainframes, as these machines are now known, were kept locked up in huge, particularly air-conditioned computer rooms, with teams of qualified operators to manage them. Only huge businesses, significant government entities, or institutions could afford the price tag of several million dollars.
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Third Generation (1965-80): In comparison to second-generation computers, which were constructed from individual transistors, the IBM 360 was the first major computer line to employ (small-scale) ICs (Integrated Circuits). As a result, it offered a significant price/performance benefit. It was an instant hit, and all the other big manufacturers quickly embraced the concept of a family of interoperable computers. All software, including the OS/360 operating system, was supposed to be compatible with all models in the original design. It had to run on massive systems, which frequently replaced 7094s for heavy computation and weather forecasting, and tiny systems, which frequently merely replaced 1401s for transferring cards to tape. Both systems with few peripherals and systems with many peripherals needed to function well with it. It had to function both in professional and academic settings. Above all, it had to be effective for each of these many applications.
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Fourth Generation (1980-Present): The personal computer era began with the creation of LSI (Large Scale Integration) circuits, processors with thousands of transistors on a square centimeter of silicon. Although personal computers, originally known as microcomputers, did not change significantly in architecture from minicomputers of the PDP-11 class, they did differ significantly in price.
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Fifth Generation (1990-Present): People have yearned for a portable communication gadget ever since detective Dick Tracy in the 1940s comic strip began conversing with his "two-way radio wristwatch." In 1946, a real mobile phone made its debut, and it weighed about 40 kilograms. The first real portable phone debuted in the 1970s and was incredibly lightweight at about one kilogram. It was jokingly referred to as "the brick." Soon, everyone was clamoring for one.
- Convenience: An OS makes a computer more convenient to use.
- Efficiency: An OS allows the computer system resources to be used efficiently.
- Ability to Evolve: An OS should be constructed in such a way as to permit the effective development, testing, and introduction of new system functions at the same time without interfering with service.
- Throughput: An OS should be constructed so that It can give maximum throughput(Number of tasks per unit time).
- Resource Management: When parallel accessing happens in the OS, it means when multiple users are accessing the system, the OS works as a Resource Manager. Its responsibility is to provide hardware to the user. It decreases the load in the system.
- Process Management: It includes various tasks like scheduling and termination of the process. OS manages various tasks at a time. Here CPU Scheduling happens means all the tasks would be done by the many algorithms that use for scheduling.
- Storage Management: The file system mechanism used for the management of the storage. NIFS, CFS, CIFS, NFS, etc. are some file systems. All the data is stored in various tracks of Hard disks that are all managed by the storage manager. It included Hard Disk.
- Memory Management: Refers to the management of primary memory. The operating system has to keep track of how much memory has been used and by whom. It has to decide which process needs memory space and how much. OS also has to allocate and deallocate the memory space.
- Security/Privacy Management: Privacy is also provided by the Operating system utilizing passwords so that unauthorized applications can't access programs or data. For example, Windows uses Kerberos authentication to prevent unauthorized access to data.
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Mainframe OS: At the high end are the operating systems for mainframes, those room-sized computers still found in major corporate data centers. These computers differ from personal computers in terms of their I/O capacity. A mainframe with 1000 disks and millions of gigabytes of data is not unusual; a personal computer with these specifications would be the envy of its friends. Mainframes are also making some- a thing of a comeback as high-end Web servers, servers for large-scale electronic commerce sites, and servers for business-to-business transactions. The operating systems for mainframes are heavily oriented toward processing many jobs at once, most of which need prodigious amounts of I/O. They typically offer three kinds of services: batch, transaction processing, and timesharing
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Server OS: One level down is the server operating systems. They run on servers, which are either very large personal computers, workstations, or even mainframes. They serve multiple users at once over a network and allow the users to share hardware and software resources. Servers can provide print service, file service, or Web service. Internet providers run many server machines to support their customers , and Websites use servers to store Web pages and handle incoming requests. Typical server operating systems are Solaris, FreeBSD, Linux, and Windows Server 201x.
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Multiprocessor OS: An increasingly common way to get major-league computing power is to connect multiple CPUs into a single system. Depending on precisely how they are connected and what is shared, these systems are called parallel computers, multi-computers, or multiprocessors. They need special operating systems, but often these are variations on the server operating systems, with special features for communication, connectivity, and consistency.
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Personal Computer OS: The next category is the personal computer operating system. Modern ones all support multiprogramming, often with dozens of programs started up at boot time. Their job is to provide good support to a single user. They are widely used for word processing, spreadsheets, games, and Internet access. Common examples are Linux, FreeBSD, Windows 7, Windows 8, and Apple's OS X. Personal computer operating systems are so widely known that probably little introduction is needed. Many people are not even aware that other kinds exist.
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Embedded OS: Embedded systems run on computers that control devices that are not generally considered computers and do not accept user-installed software. Typical examples are microwave ovens, TV sets, cars, DVD recorders, traditional phones, and MP3 players. The main property distinguishing embedded systems from handhelds is the certainty that no untrusted software will ever run on them. You cannot download new applications to your microwave oven—all the software is in ROM. This means there is no need for protection between applications, simplifying design. Systems such as Embedded Linux, QNX and VxWorks is popular in this domain.
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Smart Card OS: The smallest operating systems run on credit-card-sized smart card devices with CPU chips. They have very severe processing power and memory constraints. Some are powered by contacts in the reader into which they are inserted, but contactless smart cards are inductively powered, greatly limiting what they can do. Some can handle only a single function, such as electronic payments, but others can handle multiple functions. Often these are proprietary systems. Some smart cards are Java oriented. This means that the ROM on the smart card holds an interpreter for the Java Virtual Machine (JVM). Java applets (small programs) are downloaded to the card and are interpreted by the JVM interpreter. Some of these cards can handle multiple Java applets at the same time, leading to multiprogramming and the need to schedule them. Resource management and protection also become an issue when two or more applets are present simultaneously. These issues must be handled by the (usually extremely primitive) operating system present on the card.
The term memory refers to the component within your computer allowing short-term data access. You may recognize this component as DRAM or dynamic random-access memory. Your computer performs many operations by accessing data stored in its short-term memory. Some examples of such operations include editing a document, loading applications, and browsing the Internet. The speed and performance of your system depend on the amount of memory that is installed on your computer.
If you have a desk and a filing cabinet, the desk represents your computer's memory. Items you need to use immediately are kept on your desk for easy access. However, not much can be stored on a desk due to its size limitations.
Whereas memory refers to the location of short-term data, storage is the component within your computer that allows you to store and access data long-term. Usually, storage comes in the form of a solid-state drive or a hard drive. Storage houses your applications, operating system, and files indefinitely. Computers need to read and write information from the storage system, so the storage speed determines how fast your system can boot up, load, and access what you've saved.
While the desk represents the computer's memory, the filing cabinet represents your computer's storage. It holds items that need to be saved and stored but is not necessarily needed for immediate access. The size of the filing cabinet means that it can hold many things.
An important distinction between memory and storage is that memory clears when the computer is turned off. On the other hand, storage remains intact no matter how often you shut off your computer. Therefore, in the desk and filing cabinet analogy, any files left on your desk will be thrown away when you leave the office. Everything in your filing cabinet will remain.
At the heart of computer systems lies memory, the space where programs run and data is stored. But what happens when the programs you're running and the data you're working with exceed the physical capacity of your computer's memory? This is where virtual memory steps in, acting as a smart extension to your computer's memory and enhancing its capabilities.
Definition and Purpose of Virtual Memory:
Virtual memory is a memory management technique employed by operating systems to overcome the limitations of physical memory (RAM). It creates an illusion for software applications that they have access to a larger amount of memory than what is physically installed on the computer. In essence, it enables programs to utilize memory space beyond the confines of the computer's physical RAM.
The primary purpose of virtual memory is to enable efficient multitasking and the execution of larger programs, all while maintaining the responsiveness of the system. It achieves this by creating a seamless interaction between the physical RAM and secondary storage devices, like the hard drive or SSD.
How Virtual Memory Extends Available Physical Memory:
Think of virtual memory as a bridge that connects your computer's RAM and its secondary storage (disk drives). When you run a program, parts of it are loaded into the faster physical memory (RAM). However, not all parts of the program may be used immediately.
Virtual memory exploits this situation by moving sections of the program that aren't actively being used from RAM to the secondary storage, creating more room in RAM for the parts that are actively in use. This process is transparent to the user and the running programs. When the moved parts are needed again, they are swapped back into RAM, while other less active parts may be moved to the secondary storage.
This dynamic swapping of data in and out of physical memory is managed by the operating system. It allows programs to run even if they're larger than the available RAM, as the operating system intelligently decides what data needs to be in RAM for optimal performance.
In summary, virtual memory acts as a virtualization layer that extends the available physical memory by temporarily transferring parts of programs and data between the RAM and secondary storage. This process ensures that the computer can handle larger tasks and numerous programs simultaneously, all while maintaining efficient performance and responsiveness.
In computing, a file system or filesystem (often abbreviated to fs) is a method and data structure the operating system uses to control how data is stored and retrieved. Without a file system, data placed in a storage medium would be one large body of data with no way to tell where one piece of data stopped and the next began or where any piece of data was located when it was time to retrieve it. By separating the data into pieces and giving each piece a name, the data is easily isolated and identified. Taking its name from how a paper-based data management system is named, each data group is called a "file". The structure and logic rules used to manage the groups of data and their names are called a "file system."
There are many kinds of file systems, each with unique structure and logic, properties of speed, flexibility, security, size, and more. Some file systems have been designed to be used for specific applications. For example, the ISO 9660 file system is designed specifically for optical discs.
File systems can be used on many types of storage devices using various media. As of 2019, hard disk drives have been key storage devices and are projected to remain so for the foreseeable future. Other kinds of media that are used include SSDs, magnetic tapes, and optical discs. In some cases, such as with tmpfs, the computer's main memory (random-access memory, RAM) creates a temporary file system for short-term use.
Some file systems are used on local data storage devices; others provide file access via a network protocol (for example, NFS, SMB, or 9P clients). Some file systems are "virtual", meaning that the supplied "files" (called virtual files) are computed on request (such as procfs and sysfs) or are merely a mapping into a different file system used as a backing store. The file system manages access to both the content of files and the metadata about those files. It is responsible for arranging storage space; reliability, efficiency, and tuning with regard to the physical storage medium are important design considerations.
A file system stores and organizes data and can be thought of as a type of index for all the data contained in a storage device. These devices can include hard drives, optical drives, and flash drives.
File systems specify conventions for naming files, including the maximum number of characters in a name, which characters can be used, and, in some systems, how long the file name suffix can be. In many file systems, file names are not case-sensitive.
Along with the file itself, file systems contain information such as the file's size and its attributes, location, and hierarchy in the directory in the metadata. Metadata can also identify free blocks of available storage on the drive and how much space is available.
A file system also includes a format to specify the path to a file through the structure of directories. A file is placed in a directory -- or a folder in Windows OS -- or subdirectory at the desired place in the tree structure. PC and mobile OSes have file systems in which files are placed in a hierarchical tree structure.
Before files and directories are created on the storage medium, partitions should be put into place. A partition is a region of the hard disk or other storage that the OS manages separately. One file system is contained in the primary partition, and some OSes allow for multiple partitions on one disk. In this situation, if one file system gets corrupted, the data in a different partition will be safe.
There are several types of file systems, all with different logical structures and properties, such as speed and size. The type of file system can differ by OS and the needs of that OS. Microsoft Windows, Mac OS X, and Linux are the three most common PC operating systems. Mobile OSes include Apple iOS and Google Android.
Major file systems include the following:
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File allocation table (FAT) is supported by Microsoft Windows OS. FAT is considered simple and reliable and modeled after legacy file systems. FAT was designed in 1977 for floppy disks but was later adapted for hard disks. While efficient and compatible with most current OSes, FAT cannot match the performance and scalability of more modern file systems.
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Global file system (GFS) is a file system for the Linux OS, and it is a shared disk file system. GFS offers direct access to shared block storage and can be used as a local file system.
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GFS2 is an updated version with features not included in the original GFS, such as an updated metadata system. Under the GNU General Public License terms, both the GFS and GFS2 file systems are available as free software.
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Hierarchical file system (HFS) was developed for use with Mac operating systems. HFS can also be called Mac OS Standard, succeeded by Mac OS Extended. Originally introduced in 1985 for floppy and hard disks, HFS replaced the original Macintosh file system. It can also be used on CD-ROMs.
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The NT file system -- also known as the New Technology File System (NTFS) -- is the default file system for Windows products from Windows NT 3.1 OS onward. Improvements from the previous FAT file system include better metadata support, performance, and use of disk space. NTFS is also supported in the Linux OS through a free, open-source NTFS driver. Mac OSes have read-only support for NTFS.
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Universal Disk Format (UDF) is a vendor-neutral file system for optical media and DVDs. UDF replaces the ISO 9660 file system and is the official file system for DVD video and audio, as chosen by the DVD Forum.
Cloud computing is the ability to access information and applications over the Internet. Cloud computing allows users to access applications and data from any location with an Internet connection.
Cloud computing is a type of Internet-based computing that provides shared computer processing resources and data to computers and other devices on demand.
It is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.
Cloud computing is a big shift from how businesses think about IT resources. Here are seven common reasons organizations are turning to cloud computing services:
Cost Cloud computing eliminates the capital expense of buying hardware and software and setting up and running on-site data centers—the racks of servers, the round-the-clock electricity for power and cooling, and the IT experts for managing the infrastructure. It adds up fast.
Speed Most cloud computing services are provided self-service and on demand, so even vast amounts of computing resources can be provisioned in minutes, typically with just a few mouse clicks, giving businesses a lot of flexibility and taking the pressure off capacity planning.
Global scale The benefits of cloud computing services include the ability to scale elastically. In cloud speak, that means delivering the right amount of IT resources—for example, more or less computing power, storage, and bandwidth—right when it is needed and from the right geographic location.
Productivity On-site data centers typically require a lot of "racking and stacking"—hardware setup, software patching, and other time-consuming IT management chores. Cloud computing removes the need for many of these tasks, so IT teams can spend time on achieving more important business goals.
Performance The biggest cloud computing services run on a worldwide network of secure data centers, which are regularly upgraded to the latest generation of fast and efficient computing hardware. This offers several benefits over a single corporate data center, including reduced network latency for applications and greater economies of scale.
Reliability Cloud computing makes data backup, disaster recovery, and business continuity easier and less expensive because data can be mirrored at multiple redundant sites on the cloud provider's network.
Security Many cloud providers offer a broad set of policies, technologies, and controls that strengthen your security posture overall, helping protect your data, apps, and infrastructure from potential threats.
Machine learning is the practice of teaching a computer to learn. The concept uses pattern recognition, as well as other forms of predictive algorithms, to make judgments on incoming data. This field is closely related to artificial intelligence and computational statistics.
In this, machine learning models are trained with labeled data sets, which allow the models to learn and grow more accurately over time. For example, an algorithm would be trained with pictures of dogs and other things, all labeled by humans, and the machine would learn ways to identify pictures of dogs on its own. Supervised machine learning is the most common type used today.
Practical applications of Supervised Learning –
- Bioinformatics: Bioinformatics is the study of how individuals retain biological knowledge such as fingerprints, eye texture, earlobes, and so on. Mobile phones are now clever enough to comprehend our biological data and then verify us to increase system security.
- Speech recognition: It's the type of program where you may convey your voice to the program, and it will identify you. The most well-known real-world gadgets are digital assistants such as Google Assistant or Siri, which respond to the term only with your voice.
- Spam detection: This tool is used to prevent fictitious or machine-based communications from being sent. Gmail includes an algorithm that learns numerous wrong terms. The Oneplus Messages App asks the user to specify which terms should be prohibited, and the keyword will prevent such texts from the app.
- Object recognition for the vision: This type of software is utilized when you have to define anything. You have a big dataset that you utilize to train the algorithm, and it can recognize a new object using this.
In Unsupervised machine learning, a program looks for patterns in unlabeled data. Unsupervised machine learning can find patterns or trends that people aren't explicitly looking for. For example, an unsupervised machine learning program could look through online sales data and identify different types of clients making purchases.
Practical applications of unsupervised Learning
- Clustering: Clustering is the process of categorizing data into separate groups. When we don't know all of the details about the clusters, we can utilize unsupervised learning to cluster them. Unsupervised learning is used to analyze and organize data that doesn't have pre-labeled classes or class properties. Clustering can help firms handle their data more effectively. Suppose you have a YouTube channel. You may have a lot of information on your subscribers. If you want to find similar subscribers, you would need to use a clustering technique.
- Visualization: The process of making diagrams, photos, graphs, charts, and so on to present information is known as visualization. Unsupervised machine learning can be used to implement this strategy. Suppose you are a cricket coach with information regarding your team's performance in a tournament. You might wish to quickly locate all of the match statistics. You can pass the unlabeled and complicated data to a visualization algorithm.
- Anomaly detection: Anomaly detection is the discovery of unusual things, occurrences, or observations that raise suspicions by deviating greatly from regular data. In this situation, the system is programmed with a large number of typical cases. As a result, when it detects an unexpected occurrence, it can determine if it is an anomaly or not. Credit card fraud detection is a good illustration of this. This issue is now being addressed utilizing unsupervised machine learning anomaly detection approaches. To avoid fraud, the system identifies unexpected credit card transactions.
The disadvantage of supervised learning is that it requires hand-labeling by ML specialists or data scientists and requires a high cost to process. Unsupervised learning also has a limited spectrum for its applications. To overcome these drawbacks of supervised learning and unsupervised learning algorithms, the concept of Semi-supervised learning is introduced. Typically, this combination contains a very small amount of labeled data and a large amount of unlabelled data. The basic procedure involved is that first, the programmer will cluster similar data using an unsupervised learning algorithm and then use the existing labeled data to label the rest of the unlabelled data.
Practical applications of Semi-Supervised Learning –
- Speech Analysis: Since labeling audio files is a very intensive task, Semi-Supervised learning is a very natural approach to solve this problem.
- Internet Content: Classification: Labeling each webpage is an impractical and unfeasible process and thus uses Semi-Supervised learning algorithms. Even the Google search algorithm uses a variant of Semi-Supervised learning to rank the relevance of a webpage for a given query.
- Protein Sequence Classification: Since DNA strands are typically very large, the rise of Semi-Supervised learning has been imminent in this field.
This trains machines through trial and error to take the best action by establishing a reward system. Reinforcement learning can train models to play games or train autonomous vehicles to drive by telling the machine when it made the right decisions, which helps it learn over time what actions it should take.
Practical applications of Reinforcement Learning -
- Production Systems e.g. Google Cloud AutoML, Facebook Horizon, Recommendation, advertisement, search
- Autonomous Driving
- Business Management e.g. solving the vehicle routing problem, fraudulent behavior in e-commerce, Concurrent reinforcement learning from customer interactions
- Recommender systems e.g. for search, recommendation, and online advertising
Natural language processing is a field of machine learning in which machines learn to understand natural language as spoken and written by humans instead of the data and numbers normally used to program computers. This allows machines to recognize the language, understand it, and respond to it, as well as create new text and translate between languages. Natural language processing enables familiar technology like chatbots and digital assistants like Siri or Alexa.
Practical applications of NLP:
- Question Answering: Question Answering focuses on building systems that automatically answer the questions asked by humans in a natural language.
- Spam Detection: Spam detection is used to detect unwanted e-mails getting to a user's inbox.
- Sentiment Analysis: Sentiment Analysis is also known as opinion mining. It is used on the web to analyze the attitude, behavior, and emotional state of the sender. This application is implemented through a combination of NLP (Natural Language Processing) and statistics by assigning the values to the text (positive, negative, or natural) and identifying the mood of the context (happy, sad, angry, etc.)
- Machine Translation: Machine translation is used to translate text or speech from one natural language to another natural language. e.g. Google Translate
- Spelling correction: Microsoft Corporation provides word processor software like MS-word and PowerPoint for spelling correction.
Neural networks are a commonly used, specific class of machine learning algorithms. Artificial neural networks are modeled on the human brain, in which thousands or millions of processing nodes are interconnected and organized into layers.
In an artificial neural network, cells, or nodes, are connected, with each cell processing inputs and producing an output that is sent to other neurons. Labeled data moves through the nodes or cells, with each cell performing a different function. In a neural network trained to identify whether a picture contains a cat or not, the different nodes would assess the information and arrive at an output that indicates whether a picture features a cat.
Practical applications of Neural Networks:
- Stock Market Prediction: To make a successful stock prediction in real-time, a Multilayer Perceptron MLP (class of feedforward artificial intelligence algorithm) is employed. MLP comprises multiple layers of nodes, and each of these layers is fully connected to the succeeding nodes. Stock's past performances, annual returns, and non-profit ratios are considered for building the MLP model.
- Social Media: Multi-layered Perceptrons forecast social media trends. It uses different training methods like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Squared Error (MSE). MLP takes into consideration several factors like the user's favorite Instagram pages, bookmarked choices, etc. Post analysis of individuals' behaviors via social media networks, the data can be linked to people's spending habits. MLP ANN is used to mine data from social media applications.
- Aerospace: Aerospace Engineering is an expansive term that covers developments in spacecraft and aircraft. Fault diagnosis, high-performance auto-piloting, securing aircraft control systems, and modeling key dynamic simulations are some of the key areas that neural networks have taken over. Time delay Neural networks can be employed for modeling non-linear time dynamic systems.
Deep learning networks are neural networks with many layers. The layered network can process extensive amounts of data and determine the “weight” of each link in the network — for example, in an image recognition system, some layers of the neural network might detect individual features of a face, like eyes, nose, or mouth, while another layer would be able to tell whether those features appear in a way that indicates a face.
Practical applications of Deep Learning:
- Automatic Text Generation – Corpus of text is learned, and from this model, new text is generated, word-by-word or character-by-character. Then this model is capable of learning how to spell, punctuate, and form sentences, or it may even capture the style.
- Healthcare – Helps in diagnosing various diseases and treating them.
- Automatic Machine Translation – Certain words, sentences, or phrases in one language is transformed into another language (Deep Learning is achieving top results in the areas of text and images).
- Image Recognition – Recognizes and identifies peoples and objects in images as well as understands content and context. This area is already being used in Gaming, Retail, Tourism, etc.
- Predicting Earthquakes – Teaches a computer to perform viscoelastic computations, which are used in predicting earthquakes.
Web Technology refers to the various tools and techniques that are utilized in the process of communication between different types of devices over the Internet. A web browser is used to access web pages. Web browsers can be defined as programs that display text, data, pictures, animation, and video on the Internet. Hyperlinked resources on the World Wide Web can be accessed using software interfaces provided by Web browsers.
- World Wide Web (WWW) The World Wide Web is based on several different technologies: Web browsers, Hypertext Markup Language (HTML), and Hypertext Transfer Protocol (HTTP).
- Web Browser The web browser is an application software to explore www (World Wide Web). It provides an interface between the server and the client and requests to the server for web documents and services.
- Web Server A web server is a program that processes the network requests of the users and serves them with files that create web pages. This exchange takes place using Hypertext Transfer Protocol (HTTP).
- Web Pages A webpage is a digital document that is linked to the World Wide Web and viewable by anyone connected to the Internet who has a web browser.
- Web Development Web development refers to the building, creating, and maintaining of websites. It includes aspects such as web design, web publishing, web programming, and database management. It is the creation of an application that works over the Internet, i.e., websites.
The part of a website where the user interacts directly is termed the front end. It is also referred to as the ‘client side’ of the application.
The backend is the server side of a website. It is part of the website that users cannot see and interact with. It is the portion of software that does not come in direct contact with the users. It is used to store and arrange data.
A computer network is a set of computers sharing resources located on or provided by network nodes. Computers use common communication protocols over digital interconnections to communicate with each other. These interconnections are made up of telecommunication network technologies based on physically wired, optical, and wireless radio-frequency methods that may be arranged in a variety of network topologies.
The nodes of a computer network can include personal computers, servers, networking hardware, or other specialized or general-purpose hosts. They are identified by network addresses and may have hostnames. Hostnames serve as memorable labels for the nodes, rarely changed after the initial assignment. Network addresses serve for locating and identifying the nodes by communication protocols such as the Internet Protocol.
Computer networks may be classified by many criteria, including the transmission medium used to carry signals, bandwidth, communications protocols to organize network traffic, the network size, the topology, traffic control mechanism, and organizational intent.
There are two primary types of computer networking:
- Wired networking: Wired networking requires the use of a physical medium for transport between nodes. Copper-based Ethernet cabling, popular due to its low cost and durability, is commonly used for digital communications in businesses and homes. Alternatively, optical fiber is used to transport data over greater distances and at faster speeds, but it has several tradeoffs, including higher costs and more fragile components.
- Wireless networking: Wireless networking uses radio waves to transport data over the air, enabling devices to be connected to a network without any cabling. Wireless LANs are the most well-known and widely deployed form of wireless networking. Alternatives include microwave, satellite, cellular, and Bluetooth, among others.
OSI stands for Open Systems Interconnection. It was developed by ISO – ‘International Organization for Standardization‘in the year 1984. It is a 7-layer architecture with each layer having specific functionality to perform. All these seven layers work collaboratively to transmit the data from one person to another across the globe.
The lowest layer of the OSI reference model is the physical layer. It is responsible for the actual physical connection between the devices. The physical layer contains information in the form of bits. It is responsible for transmitting individual bits from one node to the next. When receiving data, this layer will get the signal received and convert it into 0s and 1s and send them to the Data Link layer, which will put the frame back together.
The functions of the physical layer are as follows:
- Bit synchronization: The physical layer provides the synchronization of the bits by providing a clock. This clock controls both sender and receiver thus providing synchronization at the bit level.
- Bit rate control: The Physical layer also defines the transmission rate, i.e., the number of bits sent per second.
- Physical topologies: Physical layer specifies how the different devices/nodes are arranged in a network, i.e., bus, star, or mesh topology.
- Transmission mode: Physical layer also defines how the data flows between the two connected devices. The various transmission modes possible are Simplex, half-duplex and full-duplex.
The data link layer is responsible for the node-to-node delivery of the message. The main function of this layer is to make sure data transfer is error-free from one node to another over the physical layer. When a packet arrives in a network, it is the responsibility of the DLL to transmit it to the host using its MAC address.
The Data Link Layer is divided into two sublayers:
- Logical Link Control (LLC)
- Media Access Control (MAC)
The packet received from the Network layer is further divided into frames depending on the frame size of the NIC(Network Interface Card). DLL also encapsulates Sender and Receiver’s MAC address in the header.
The Receiver’s MAC address is obtained by placing an ARP(Address Resolution Protocol) request onto the wire asking, “Who has that IP address?” and the destination host will reply with its MAC address.
The functions of the Data Link layer are :
- Framing: Framing is a function of the data link layer. It provides a way for a sender to transmit a set of bits that are meaningful to the receiver. This can be accomplished by attaching special bit patterns to the beginning and end of the frame.
- Physical Addressing: After creating frames, the Data link layer adds physical addresses (MAC addresses) of the sender and/or receiver in the header of each frame.
- Error control: Data link layer provides the mechanism of error control in which it detects and retransmits damaged or lost frames.
- Flow Control: The data rate must be constant on both sides, or else the data may get corrupted; thus, flow control coordinates the amount of data that can be sent before receiving an acknowledgment.
- Access control: When a single communication channel is shared by multiple devices, the MAC sub-layer of the data link layer helps to determine which device has control over the channel at a given time.
The network layer works for the transmission of data from one host to the other located in different networks. It also takes care of packet routing, i.e., the selection of the shortest path to transmit the packet from the number of routes available. The sender & receiver’s IP addresses are placed in the header by the network layer.
The functions of the Network layer are :
- Routing: The network layer protocols determine which route is suitable from source to destination. This function of the network layer is known as routing.
- Logical Addressing: To identify each device on internetwork uniquely, the network layer defines an addressing scheme. The sender & receiver’s IP addresses are placed in the header by the network layer. Such an address distinguishes each device uniquely and universally.
The Internet is a global system of interconnected computer networks that use the standard Internet protocol suite (TCP/IP) to serve billions of users worldwide. It is a network of networks that consists of millions of private, public, academic, business, and government networks of local to global scope that is linked by a broad array of electronic, wireless, and optical networking technologies. The Internet carries an extensive range of information resources and services, such as the interlinked hypertext documents and applications of the World Wide Web (WWW) and the infrastructure to support email.
The World Wide Web (WWW) is an information space where documents and other web resources are identified by Uniform Resource Locators (URLs), interlinked by hypertext links, and accessible via the Internet. English scientist Tim Berners-Lee invented the World Wide Web in 1989. He wrote the first web browser in 1990 while employed at CERN in Switzerland. The browser was released outside CERN in 1991, first to other research institutions starting in January 1991 and to the general public on the Internet in August 1991.
The Internet Protocol (IP) is a protocol, or set of rules, for routing and addressing packets of data so that they can travel across networks and arrive at the correct destination. Data traversing the Internet is divided into smaller pieces called packets.
A database is a collection of related data that represents some aspect of the real world. A database system is designed to be built and populated with data for a certain task.
Database Management System (DBMS) is software for storing and retrieving users' data while considering appropriate security measures. It consists of a group of programs that manipulate the database. The DBMS accepts the request for data from an application and instructs the operating system to provide the specific data. In large systems, a DBMS helps users and other third-party software store and retrieve data.
DBMS allows users to create their databases as per their requirements. The term "DBMS" includes the use of a database and other application programs. It provides an interface between the data and the software application.
Let us see a simple example of a university database. This database maintains information concerning students, courses, and grades in a university environment. The database is organized into five files:
- The STUDENT file stores the data of each student
- The COURSE file stores contain data on each course.
- The SECTION stores the information about sections in a particular course.
- The GRADE file stores the grades which students receive in the various sections
- The TUTOR file contains information about each professor.
To define DBMS:
- We need to specify the structure of the records of each file by defining the different types of data elements to be stored in each record.
- We can also use a coding scheme to represent the values of a data item.
- Basically, your database will have five tables with a foreign key defined amongst the various tables.
Here are the important landmarks from history:
- 1960 – Charles Bachman designed the first DBMS system
- 1970 – Codd introduced IBM'S Information Management System (IMS)
- 1976- Peter Chen coined and defined the Entity-relationship model, also known as the ER model
- 1980 – Relational Model becomes a widely accepted database component
- 1985- Object-oriented DBMS develops.
- 1990s- Incorporation of object orientation in relational DBMS.
- 1991- Microsoft ships MS access, a personal DBMS that displaces all other personal DBMS products.
- 1995: First Internet database applications
- 1997: XML applied to database processing. Many vendors begin to integrate XML into DBMS products.
Here are the characteristics and properties of a Database Management System:
- Provides security and removes redundancy
- Self-describing the nature of a database system
- Insulation between programs and data abstraction
- Support of multiple views of the data
- Sharing of data and multi-user transaction processing
- Database Management Software allows entities and relations among them to form tables.
- It follows the ACID concept ( Atomicity, Consistency, Isolation, and Durability).
- DBMS supports a multi-user environment that allows users to access and manipulate data in parallel.
Here is the list of some popular DBMS systems:
- MySQL
- Microsoft Access
- Oracle
- PostgreSQL
- dBASE
- FoxPro
- SQLite
- IBM DB2
- LibreOffice Base
- MariaDB
- Microsoft SQL Server etc.
Cryptography is a technique to secure data and communication. It is a method of protecting information and communications through the use of codes so that only those for whom the information is intended can read and process it. Cryptography is used to protect data in transit, at rest, and in use. The prefix crypt means "hidden" or "secret", and the suffix graphy means "writing".
There are two types of cryptography:
Cryptocurrency is a digital currency in which encryption techniques are used to regulate the generation of units of currency and verify the transfer of funds, operating independently of a central bank. Cryptocurrencies use decentralized control as opposed to centralized digital currency and central banking systems. The decentralized control of each cryptocurrency works through distributed ledger technology, typically a blockchain, that serves as a public financial transaction database. A defining feature of a cryptocurrency, and arguably its most endearing allure, is its organic nature; it is not issued by any central authority, rendering it theoretically immune to government interference or manipulation.
In theoretical computer science and mathematics, the theory of computation is the branch that deals with what problems can be solved on a model of computation using an algorithm, how efficiently they can be solved, or to what degree (e.g., approximate solutions versus precise ones). The field is divided into three major branches: automata theory and formal languages, computability theory, and computational complexity theory, which are linked by the question: "What are the fundamental capabilities and limitations of computers?".
Automata theory is the study of abstract machines and automata, as well as the computational problems that can be solved using them. It is a theory in theoretical computer science. The word automata comes from the Greek word αὐτόματος, which means "self-acting, self-willed, self-moving". An automaton (automata in plural) is an abstract self-propelled computing device that follows a predetermined sequence of operations automatically. An automaton with a finite number of states is called a Finite Automaton (FA) or Finite-State Machine (FSM). The figure on the right illustrates a finite-state machine, which is a well-known type of automaton. This automaton consists of states (represented in the figure by circles) and transitions (represented by arrows). As the automaton sees a symbol of input, it makes a transition (or jump) to another state, according to its transition function, which takes the previous state and current input symbol as its arguments.
In logic, mathematics, computer science, and linguistics, a formal language consists of words whose letters are taken from an alphabet and are well-formed according to a specific set of rules.
The alphabet of a formal language consists of symbols, letters, or tokens that concatenate into strings of the language.[1] Each string concatenated from symbols of this alphabet is called a word, and the words that belong to a particular formal language are sometimes called well-formed words or well-formed formulas. A formal language is often defined using formal grammar, such as regular grammar or context-free grammar, which consists of its formation rules.
In computer science, formal languages are used, among others, as the basis for defining the grammar of programming languages and formalized versions of subsets of natural languages in which the words of the language represent concepts that are associated with particular meanings or semantics. In computational complexity theory, decision problems are typically defined as formal languages and complexity classes are defined as the sets of formal languages that can be parsed by machines with limited computational power. In logic and the foundations of mathematics, formal languages are used to represent the syntax of axiomatic systems, and mathematical formalism is the philosophy that all mathematics can be reduced to the syntactic manipulation of formal languages in this way.
Computability theory, also known as recursion theory, is a branch of mathematical logic, computer science, and the theory of computation that originated in the 1930s with the study of computable functions and Turing degrees. The field has since expanded to include the study of generalized computability and definability. In these areas, computability theory overlaps with the proof theory and effective descriptive set theory.
In theoretical computer science and mathematics, computational complexity theory focuses on classifying computational problems according to their resource usage and relating these classes to each other. A computational problem is a task solved by a computer. A computation problem is solvable by a mechanical application of mathematical steps, such as an algorithm.
A problem is regarded as inherently difficult if its solution requires significant resources, whatever the algorithm used. The theory formalizes this intuition by introducing mathematical models of computation to study these problems and quantifying their computational complexity, i.e., the number of resources needed to solve them, such as time and storage. Other measures of complexity are also used, such as the amount of communication (used in communication complexity), the number of gates in a circuit (used in circuit complexity), and the number of processors (used in parallel computing). One of the roles of computational complexity theory is to determine the practical limits on what computers can and cannot do. The P versus NP problem, one of the seven Millennium Prize Problems, is dedicated to the field of computational complexity.[1]
Closely related fields in theoretical computer science are the analysis of algorithms and computability theory. A key distinction between the analysis of algorithms and computational complexity theory is that the former is devoted to analyzing the number of resources needed by a particular algorithm to solve a problem, whereas the latter asks a more general question about all possible algorithms that could be used to solve the same problem. More precisely, computational complexity theory tries to classify problems that can or cannot be solved with appropriately restricted resources. In turn, imposing restrictions on the available resources is what distinguishes computational complexity from computability theory: the latter theory asks what kinds of problems can, in principle, be solved algorithmically.