A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
The presentation is made on CNN's which is explained using the image classification problem, the presentation was prepared in perspective of understanding computer vision and its applications. I tried to explain the CNN in the most simple way possible as for my understanding. This presentation helps the beginners of CNN to have a brief idea about the architecture and different layers in the architecture of CNN with the example. Please do refer the references in the last slide for a better idea on working of CNN. In this presentation, I have also discussed the different types of CNN(not all) and the applications of Computer Vision.
Basic definitions, terminologies, and Working of ANN has been explained. This ppt also shows how ANN can be performed in matlab. This material contains the explanation of Feed forward back propagation algorithm in detail.
Part 1 of the Deep Learning Fundamentals Series, this session discusses the use cases and scenarios surrounding Deep Learning and AI; reviews the fundamentals of artificial neural networks (ANNs) and perceptrons; discuss the basics around optimization beginning with the cost function, gradient descent, and backpropagation; and activation functions (including Sigmoid, TanH, and ReLU). The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
An overview of Deep Learning With Neural Networks. Use cases of Deep learning and it's development. Basic introduction tp the layers of Neural Networks.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
The next phase of Smart Network Convergence could be putting Deep Learning systems on the Internet. Deep Learning and Blockchain Technology might be combined in the smart networks of the future for automated identification (deep learning) and automated transaction (blockchain). Large scale future-class problems might be addressed with Blockchain Deep Learning nets as an advanced computational infrastructure, challenges such as million-member genome banks, energy storage markets, global financial risk assessment, real-time voting, and asteroid mining.
Blockchain Deep Learning nets and Smart Networks more generally are computing networks with intelligence built in such that identification and transfer is performed by the network itself through sophisticated protocols that automatically identify (deep learning), and validate, confirm, and route transactions (blockchain) within the network.
The presentation is made on CNN's which is explained using the image classification problem, the presentation was prepared in perspective of understanding computer vision and its applications. I tried to explain the CNN in the most simple way possible as for my understanding. This presentation helps the beginners of CNN to have a brief idea about the architecture and different layers in the architecture of CNN with the example. Please do refer the references in the last slide for a better idea on working of CNN. In this presentation, I have also discussed the different types of CNN(not all) and the applications of Computer Vision.
Basic definitions, terminologies, and Working of ANN has been explained. This ppt also shows how ANN can be performed in matlab. This material contains the explanation of Feed forward back propagation algorithm in detail.
Part 1 of the Deep Learning Fundamentals Series, this session discusses the use cases and scenarios surrounding Deep Learning and AI; reviews the fundamentals of artificial neural networks (ANNs) and perceptrons; discuss the basics around optimization beginning with the cost function, gradient descent, and backpropagation; and activation functions (including Sigmoid, TanH, and ReLU). The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
An overview of Deep Learning With Neural Networks. Use cases of Deep learning and it's development. Basic introduction tp the layers of Neural Networks.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
The next phase of Smart Network Convergence could be putting Deep Learning systems on the Internet. Deep Learning and Blockchain Technology might be combined in the smart networks of the future for automated identification (deep learning) and automated transaction (blockchain). Large scale future-class problems might be addressed with Blockchain Deep Learning nets as an advanced computational infrastructure, challenges such as million-member genome banks, energy storage markets, global financial risk assessment, real-time voting, and asteroid mining.
Blockchain Deep Learning nets and Smart Networks more generally are computing networks with intelligence built in such that identification and transfer is performed by the network itself through sophisticated protocols that automatically identify (deep learning), and validate, confirm, and route transactions (blockchain) within the network.
An Artificial Neural Network (ANN) is a computational model inspired by the structure and functioning of the human brain's neural networks. It consists of interconnected nodes, often referred to as neurons or units, organized in layers. These layers typically include an input layer, one or more hidden layers, and an output layer.
TFFN: Two Hidden Layer Feed Forward Network using the randomness of Extreme L...Nimai Chand Das Adhikari
The learning speed of the feed forward neural
network takes a lot of time to be trained which is a major
drawback in their applications since the past decades. The
key reasons behind may be due to the slow gradient-based
learning algorithms which are extensively used to train the
neural networks or due to the parameters in the networks
which are tuned iteratively using some learning algorithms.
Thus, in order to eradicate the above pitfalls, a new learning
algorithm was proposed known as Extreme Learning Machines
(ELM). This algorithm tries to compute Hidden-layer-output
matrix that is made of randomly assigned input layer and
hidden layer weights and randomly assigned biases. Unlike the
other feedforward networks, ELM has the access of the whole
training dataset before going into the computation part. Here,
we have devised a new two-layer-feedforward network (TFFN)
for ELM in a new manner with randomly assigning the weights
and biases in both the hidden layers, which then calculates the
output-hidden layer weights using the Moore-Penrose generalized
inverse. TFFN doesn’t restricts the algorithm to fix the number
of hidden neurons that the algorithm should have. Rather it
searches the space which gives an optimized result in the neurons
combination in both the hidden layers. This algorithm provides a
good generalization capability than the parent Extreme Learning
Machines at an extremely fast learning speed. Here, we have
experimented the algorithm on various types of datasets and
various popular algorithm to find the performances and report
a comparison.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
2. Index
What is the Multi-layer perceptron
Why MLP
Architecture of MLP
Who its work
Application of MLP
Example
3. Multi-layer perceptron
MLP is a class of feedforward artificial neural networks. An
MLP consists of, at least, three layers of nodes: an input
layer, a hidden layer and an output layer. Except for the
input nodes, each node is a neuron that uses a nonlinear
activation function. MLP utilizes a supervised learning
technique called backpropagation for training.
MLP useful in research for their ability to solve problems
stochastically.
4.
x1
x2
Xn
A neuron can have any number of inputs from one to n, where n is the
total number of inputs.
The inputs may be represented therefore as x1, x2, x3… xn.
And the corresponding weights for the inputs as w1, w2, w3… wn
Output a = x1w1+x2w2+x3w3... +xnwn
process output
Activation
function
weight
w0
-1
+1
6. t/f
t/f
z
Sin(x)= 1 if x ≥ 0
0 if x < 0
OR T F
F
T
T F
TT
output
XOR T F
F
T
T F
TF
7. Why MLP
Single neurons are notable to solve complex tasks(e.g.
restricted to linear calculations).
Creating networks by hand is too expensive we want to
learn from data.
We want to have a generic model that can adapt to
some training data.
8. Architecture of MLP
Input layer
Hidden layer
Output layer
A multi layer perceptron's (MLP) is a finite acyclic graph.
The nodes are neurons with logistic activation.
Summation transformation
S=∑w.x
∫(s)= 1
1+e-s
9. Connection layers
• No direct connections between input and output layers.
• Fully connected between layers.
• Number of output units need not equal number of input
units.
• Number of hidden units per layer can be more or less than
input or output units.
10. Who its work
The input value are presented to the perceptron and if the
prediction output is the same as the desired output, then the
performance to the weights are made.
However, if the output doesn’t match the desired output the
weights need to be changed to reduce the error.
∆W=b*d*x
d: predicted output (desired output)
b: learning rate, usually less than 1 (beta time)
X: input data
11. Application of MLP
MLPs are useful in research for their ability to solve
problems stochastically, which often allows approximate
solutions for extremely complex problems like fitness
approximation.
In MLPs can be used to create mathematical models by
regression analysis.
MLPs make good classifier algorithms.
If we are assume that we have this canvas and we have a whole bunch of point in that canvas and we draw a line between them and we trying to classify some point that are one side of the line and the some other points that are only another side of line.
we can call it neuron or processor and receiving input it had from x0 and x1
Each one of these inputs was connected to the processor with the weight and the processor created a sum of all the inputs multiplied by the weight. That weight sum is passed through an activation function to generate the output.
The question here what is the limit here so the idea is that in different machine learning applications let’s take a very classic classification algorithms.
when we say, if we have a handwritten digits like numbers (8) and I have all the pixel of this digits and I want these pixel input to the perceptron and I want the output tell me a set of probabilities.
So the idea here that take a random number and put it in the input like 28*28=784 pixel image of grayscale values and those they are coming into processor which was wait to sudden and get the output
So, if I have a hole bunch more inputs and a hole bunch of outputs but still have a single processor unit, the reason that can came to published a book in 1969 by Marvin Minkey and Seymour that said the single perceptron can only solve the linearly separable problem.
So let’s think about this over here a linearly separable problem meaning I need to classify this stuff If I were to visualize all that stuff I can draw a line between stuff in this class and stuff in that class .
That’s mean is And and or is linearly separable problem
We found the or and and are separable to linearly perceptron
By assume that this node is AND can automatically give me the output And
But if we connected by another perceptron in OR connecting every node can give the connection of that node
So this perceptron cannot solve AND this perceptron can solve OR. The idea here are more complex problems that are not linearly separable can be solve by linked a multi layer perceptron .
In this case it should be gone more further step to Multi-layer perceptron
And talking about the logical gate AND OR and XOR
No direct connections between input and output layers.
Fully connected between layers.
Often more than 3 layers.
Number of output units need not equal number of input units.
Number of hidden units per layer can be more or less than input or output units.