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There are two popular networking models: the OSI layers model and the TCP/IP layers model. The presentation layer and session layer exist only in the OSI layers models. The TCP/IP layers model merges them into the application layer.
The presentation layer is the sixth layer of the OSI Reference model. It defines how data and information is transmitted and presented to the user. It translates data and format code in such a way that it is correctly used by the application layer.
It identifies the syntaxes that different applications use and formats data using those syntaxes. For example, a web browser receives a web page from a web server in the HTML language. HTML language includes many tags and markup that have no meaning for the end user but they have special meaning for the web browser. the web browser uses the presentation layer's logic to read those syntaxes and format data in such a way the web server wants it to be present to the user.
On the sender device, it encapsulates and compresses data before sending it to the network to increase the speed and security of the network. On the receiver device, it de-encapsulates and decompresses data before presenting it to the user.
Example standards for representing graphical information: JPEG, GIF, JPEG, and TIFF.
Example standards for representing audio information: WAV, MIDI, MP3.
Example standards for representing video information: WMV, MOV, MP4, MPEG.
Example standards for representing text information: doc, xls, txt, pdf.
The session layer is the fifth layer of the OSI layers model. It is responsible for initiating, establishing, managing, and terminating sessions between the local application and the remote applications.
It defines standards for three modes of communication: full duplex, half-duplex, and simplex.
In the full duplex mode, both devices can send and receive data simultaneously. The internet connection is an example of the full duplex mode.
In the half duplex mode, only one device can send data at a time. A telephone conversation is an example of the half-duplex mode.
In the simplex mode, only one device can send data. A radio broadcast is an example of the simplex mode.
Structure Query Language (SQL), Remote Procedure Call (RPC), and Network File System (NFS) are examples of the session layer.
By ComputerNetworkingNotes Updated on 2023-04-25
ComputerNetworkingNotes CCNA Study Guide Presentation layer and Session layer of the OSI model
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The XOR (exclusive OR) is a simple logic gate problem that cannot be solved using a single-layer perceptron (a basic neural network model). We can solve this using neural networks. Neural networks are powerful tools in machine learning.
In this article, we are going to discuss what is XOR problem, how we can solve it using neural networks, and also a simple code to demonstrate this.
Table of Content
Why single-layer perceptrons fail, how multi-layer neural networks solve xor, mathematics behind the mlp solution, geometric interpretation, training the neural network to solve xor problem.
The XOR operation is a binary operation that takes two binary inputs and produces a binary output. The output of the operation is 1 only when the inputs are different.
Below is the truth table for XOR:
Input A | Input B | XOR Output |
---|---|---|
0 | 0 | 0 |
0 | 1 | 1 |
1 | 0 | 1 |
1 | 1 | 0 |
The main problem is that a single-layer perceptron cannot solve this problem because the data is not linearly separable i.e. we cannot draw a straight line to separate the output classes (0s and 1s)
A single-layer perceptron can solve problems that are linearly separable by learning a linear decision boundary.
Mathematically, the decision boundary is represented by:
[Tex]y = \text{step}(\mathbf{w} \cdot \mathbf{x} + b)[/Tex]
For linearly separable data, the perceptron can adjust the weights [Tex]\mathbf{w}[/Tex] and bias [Tex]b[/Tex] during training to correctly classify the data. However, because XOR is not linearly separable, no single line (or hyperplane) can separate the outputs 0 and 1, making a single-layer perceptron inadequate for solving the XOR problem.
A multi-layer neural network which is also known as a feedforward neural network or multi-layer perceptron is able to solve the XOR problem. There are multiple layer of neurons such as input layer, hidden layer, and output layer.
The working of each layer:
Let’s break down the mathematics behind how an MLP can solve the XOR problem.
Consider an MLP with two neurons in the hidden layer, each applying a non-linear activation function (like the sigmoid function). The output of the hidden neurons can be represented as:
[Tex]h_1 = \sigma(w_{11} A + w_{12} B + b_1)[/Tex]
[Tex]h_2 = \sigma(w_{21} A + w_{22} B + b_2)[/Tex]
Activation functions such as the sigmoid or ReLU (Rectified Linear Unit) introduce non-linearity into the model. It enables the neural network to handle complex patterns like XOR. Without these functions, the network would behave like a simple linear model, which is insufficient for solving XOR.
The output neuron combines the outputs of the hidden neurons to produce the final output:
[Tex]\text{Output} = \sigma(w_{31} h_1 + w_{32} h_2 + b_3)[/Tex]
Where [Tex]w_{3i}[/Tex] are the weights from the hidden neurons to the output neuron, and [Tex]b_3[/Tex] is the bias for the output neuron.
During the training process, the network adjusts the weights [Tex]w_{ij}[/Tex] and biases [Tex]b_i[/Tex] using backpropagation and gradient descent to minimize the error between the predicted output and the actual XOR output.
Example Configuration:
Let’s consider a specific configuration of weights and biases that solves the XOR problem:
With these weights and biases, the network produces the correct XOR output for each input pair (A, B).
In the hidden layer, the network effectively transforms the input space into a new space where the XOR problem becomes linearly separable. This can be visualized as bending or twisting the input space such that the points corresponding to different XOR outputs (0s and 1s) are now separable by a linear decision boundary.
The neural network learns to solve the XOR problem by adjusting the weights during training. This is done using backpropagation, where the network calculates the error in its output and adjusts its internal weights to minimize this error over time. This process continues until the network can correctly predict the XOR output for all given input combinations.
The following python code implementation demonstrates how neural networks solve the XOR problem using TensorFlow and Keras:
import numpy as np import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense # Define the XOR input and output data X = np . array ([[ 0 , 0 ], [ 0 , 1 ], [ 1 , 0 ], [ 1 , 1 ]]) y = np . array ([[ 0 ], [ 1 ], [ 1 ], [ 0 ]]) # Build the neural network model model = Sequential () model . add ( Dense ( 2 , input_dim = 2 , activation = 'relu' )) # Hidden layer with 2 neurons model . add ( Dense ( 1 , activation = 'sigmoid' )) # Output layer with 1 neuron # Compile the model model . compile ( optimizer = 'adam' , loss = 'binary_crossentropy' , metrics = [ 'accuracy' ]) # Train the model model . fit ( X , y , epochs = 10000 , verbose = 0 ) # Evaluate the model _ , accuracy = model . evaluate ( X , y ) print ( f "Accuracy: { accuracy * 100 : .2f } %" ) # Make predictions predictions = model . predict ( X ) predictions = np . round ( predictions ) . astype ( int ) print ( "Predictions:" ) for i in range ( len ( X )): print ( f "Input: { X [ i ] } => Predicted Output: { predictions [ i ] } , Actual Output: { y [ i ] } " )
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 168ms/step - accuracy: 0.5000 - loss: 0.6931 Accuracy: 50.00% 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step Predictions: Input: [0 0] => Predicted Output: [0], Actual Output: [0] Input: [0 1] => Predicted Output: [0], Actual Output: [1] Input: [1 0] => Predicted Output: [0], Actual Output: [1] Input: [1 1] => Predicted Output: [0], Actual Output: [0]
The XOR problem is a classic example that highlights the limitations of simple neural networks and the need for multi-layer architectures. By introducing a hidden layer and non-linear activation functions, an MLP can solve the XOR problem by learning complex decision boundaries that a single-layer perceptron cannot. Understanding this solution provides valuable insight into the power of deep learning models and their ability to tackle non-linear problems in various domains.
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IMAGES
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Prerequisite : OSI Model. Introduction : Presentation Layer is the 6th layer in the Open System Interconnection (OSI) model. This layer is also known as Translation layer, as this layer serves as a data translator for the network. The data which this layer receives from the Application Layer is extracted and manipulated here as per the required ...
Conclusion. In conclusion, the OSI (Open Systems Interconnection) model is a conceptual framework that standardizes the functions of a telecommunication or computing system into seven distinct layers: Physical, Data Link, Network, Transport, Session, Presentation, and Application.
2. Data link layer. 1. Physical layer. v. t. e. In the seven-layer OSI model of computer networking, the presentation layer is layer 6 and serves as the data translator for the network. [ 2][ 3][ 4] It is sometimes called the syntax layer. [ 5]
Chloe Tucker. This article explains the Open Systems Interconnection (OSI) model and the 7 layers of networking, in plain English. The OSI model is a conceptual framework that is used to describe how a network functions. In plain English, the OSI model helped standardize the way computer systems send information to each other.
The presentation layer is the 6 th layer from the bottom in the OSI model. This layer presents the incoming data from the application layer of the sender machine to the receiver machine. It converts one format of data to another format of data if both sender and receiver understand different formats; hence this layer is also called the ...
The tool that manages Hypertext Transfer Protocol is an example of a program that loosely adheres to the presentation layer of OSI.Although it's technically considered an application-layer protocol per the TCP/IP model, HTTP includes presentation layer services within it.HTTP works when the requesting device forwards user requests passed to the web browser onto a web server elsewhere in the ...
The presentation layer is the lowest layer at which application programmers consider data structure and presentation, instead of simply sending data in the form of datagrams or packets between hosts. This layer deals with issues of string representation - whether they use the Pascal method (an integer length field followed by the specified ...
The layers (from bottom to top) are: Physical, Data Link, Network, Transport, Session, Presentation, and Application. Foundry. It wasn't always this way. Conceived in the 1970s when computer ...
The presentation layer structures data that is passed down from the application layer into a format suitable for network transmission. This layer is responsible for data encryption, data compression, character set conversion, interpretation of graphics commands, and so on. The network redirector also functions at this layer.
The presentation layer is layer-6 of the OSI reference model. This layer mainly responds to the service requests from the application layer (that is layer-7) and issues the service requests to layer-6 that is (the session layer). This layer mainly acts as the translator of the network. Another name of the presentation layer is the Syntax layer.
The Presentation Layer. The sixth layer of the OSI model is the Presentation layer. Applications running on the local system may or may not understand the format that is used to transmit the data over the network. The presentation layer works as a translator.
Understanding the layers of the Open Systems Interconnect (OSI) model can help users conceptualize data communication over a network. Layer 6 in the OSI model - the presentation layer - translates, compresses, and encrypts data across networks. In this article, we'll explain what the presentation layer is, how it works, and its functions and protocols.
How a single bit travels from one computer to the next is a complex concept. In 1984, the open systems interconnection (OSI) model was published as a framework for network communication. The model breaks down computer network communication into seven layers. All of the layers work together to create a digital message.
Presentation layer: The OSI presentation layer is the sixth layer and translates data across the network. Application layer: Topmost seventh layer which stipulates the interface methods for the ...
Functionalities of the Presentation Layer. Specific functionalities of the presentation layer are as follows: 1. Translation. The processes or running programs in two machines are usually exchanging the information in the form of numbers, character strings and so on before being transmitted. The information should be changed to bitstreams ...
The Presentation Layer of OSI Model - The presentation layer (Layer 6) ensures that the message is presented to the upper layer in a standardized format. It deals with the syntax and the semantics of the messages.The main functions of the presentation layer are as follows −It encodes the messages from the user dependent format to the.
Telnet (Telecommunication Network): Telnet protocol was introduced in 1969, and it offers the command line interface for making communication along with remote device or server. Tox: The Tox protocol is sometimes regarded as part of both the presentation and application layer, and it is used for sending peer-to-peer instant-messaging as well as video calling.
Data Link Layer. Network Layer. Transport Layer. Session Layer. Presentation Layer. Application Layer. Summary. The Open Systems Interconnection (OSI) networking model defines a conceptual framework for communications between computer systems. The model is an ISO standard which identifies seven fundamental networking layers, from the physical ...
6) Presentation Layer. A Presentation layer is mainly concerned with the syntax and semantics of the information exchanged between the two systems. It acts as a data translator for a network. This layer is a part of the operating system that converts the data from one presentation format to another format. The Presentation layer is also known ...
The presentation layer is the sixth layer of the OSI Reference model. It defines how data and information is transmitted and presented to the user. It translates data and format code in such a way that it is correctly used by the application layer. It identifies the syntaxes that different applications use and formats data using those syntaxes.
The Network Layer is the 5th Layer from the top and the 3rd layer from the Bottom of the OSI Model. It is one of the most important layers which plays a key role in data transmission. The main job of this layer is to maintain the quality of the data and pass and transmit it from its source to its destination.
This video covers the various functions and design issues of presentation layer. It is sixth layer in OSI reference model. Happy Learning!!
Application Layer is the topmost layer in the Open System Interconnection (OSI) model. This layer provides several ways for manipulating the data (information) which actually enables any type of user to access network with ease. This layer also makes a request to its bottom layer, which is presentation layer for receiving various types of ...
Input Layer: This layer takes the two inputs (A and B). Hidden Layer: This layer applies non-linear activation functions to create new, transformed features that help separate the classes. Output Layer: This layer produces the final XOR result. Mathematics Behind the MLP Solution. Let's break down the mathematics behind how an MLP can solve ...