The document provides an overview of neural networks for data mining. It discusses how neural networks can be used for classification tasks in data mining. It describes the structure of a multi-layer feedforward neural network and the backpropagation algorithm used for training neural networks. The document also discusses techniques like neural network pruning and rule extraction that can optimize neural network performance and interpretability.
This Presentation covers Data Mining: Classification and Prediction, NEURAL NETWORK REPRESENTATION, NEURAL NETWORK APPLICATION DEVELOPMENT, BENEFITS AND LIMITATIONS OF NEURAL NETWORKS, Neural Networks, Real Estate Appraiser, Kinds of Data Mining Problems, Data Mining Techniques, Learning in ANN, Elements of ANN, Neural Network Architectures Recurrent Neural Networks and ANN Software.
This Presentation covers Data Mining: Classification and Prediction, NEURAL NETWORK REPRESENTATION, NEURAL NETWORK APPLICATION DEVELOPMENT, BENEFITS AND LIMITATIONS OF NEURAL NETWORKS, Neural Networks, Real Estate Appraiser, Kinds of Data Mining Problems, Data Mining Techniques, Learning in ANN, Elements of ANN, Neural Network Architectures Recurrent Neural Networks and ANN Software.
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.
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
Decision tree induction \ Decision Tree Algorithm with Example| Data scienceMaryamRehman6
This Decision Tree Algorithm in Machine Learning Presentation will help you understand all the basics of Decision Tree along with what Machine Learning is, what Machine Learning is, what Decision Tree is, the advantages and disadvantages of Decision Tree, how Decision Tree algorithm works with resolved examples, and at the end of the decision Tree use case/demo in Python for loan payment. For both beginners and experts who want to learn Machine Learning Algorithms, this Decision Tree tutorial is perfect.
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.
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
Decision tree induction \ Decision Tree Algorithm with Example| Data scienceMaryamRehman6
This Decision Tree Algorithm in Machine Learning Presentation will help you understand all the basics of Decision Tree along with what Machine Learning is, what Machine Learning is, what Decision Tree is, the advantages and disadvantages of Decision Tree, how Decision Tree algorithm works with resolved examples, and at the end of the decision Tree use case/demo in Python for loan payment. For both beginners and experts who want to learn Machine Learning Algorithms, this Decision Tree tutorial is perfect.
An ANN depends on an assortment of associated units or hubs called fake neurons, which freely model the neurons in an organic cerebrum. Every association, similar to the neurotransmitters in an organic cerebrum, can send a sign to different neurons. A counterfeit neuron that gets a sign at that point measures it and can flag neurons associated with it.
Image classification is perhaps the most important part of digital image analysis. In this paper, we compare the most widely used model CNN Convolutional Neural Network , and MLP Multilayer Perceptron . We aim to show how both models differ and how both models approach towards the final goal, which is image classification. Souvik Banerjee | Dr. A Rengarajan "Hand-Written Digit Classification" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42444.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42444/handwritten-digit-classification/souvik-banerjee
X-TREPAN: A MULTI CLASS REGRESSION AND ADAPTED EXTRACTION OF COMPREHENSIBLE D...cscpconf
In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees from neural networks. We empirically evaluated the performance of the algorithm on a set of databases from real world events. This benchmark enhancement was achieved by adapting Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The models are then compared with X-TREPAN for comprehensibility and classification accuracy. Furthermore, we validate the experimentations by applying statistical methods. Finally, the modified algorithm is extended to work with multi-class regression problems and the ability to comprehend generalized feed forward networks is achieved.
X-TREPAN : A Multi Class Regression and Adapted Extraction of Comprehensible ...csandit
In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees
from neural networks. We empirically evaluated the performance of the algorithm on a set of
databases from real world events. This benchmark enhancement was achieved by adapting
Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The
models are then compared with X-TREPAN for comprehensibility and classification accuracy.
Furthermore, we validate the experimentations by applying statistical methods. Finally, the
modified algorithm is extended to work with multi-class regression problems and the ability to
comprehend generalized feed forward networks is achieved.
Deep Learning Interview Questions And Answers | AI & Deep Learning Interview ...Simplilearn
This Deep Learning interview questions and answers presentation will help you prepare for Deep Learning interviews. This presentation is ideal for both beginners as well as professionals who are appearing for Deep Learning, Machine Learning or Data Science interviews. Learn what are the most important Deep Learning interview questions and answers and know what will set you apart in the interview process.
Some of the important Deep Learning interview questions are listed below:
1. What is Deep Learning?
2. What is a Neural Network?
3. What is a Multilayer Perceptron (MLP)?
4. What is Data Normalization and why do we need it?
5. What is a Boltzmann Machine?
6. What is the role of Activation Functions in neural network?
7. What is a cost function?
8. What is Gradient Descent?
9. What do you understand by Backpropagation?
10. What is the difference between Feedforward Neural Network and Recurrent Neural Network?
11. What are some applications of Recurrent Neural Network?
12. What are Softmax and ReLU functions?
13. What are hyperparameters?
14. What will happen if learning rate is set too low or too high?
15. What is Dropout and Batch Normalization?
16. What is the difference between Batch Gradient Descent and Stochastic Gradient Descent?
17. Explain Overfitting and Underfitting and how to combat them.
18. How are weights initialized in a network?
19. What are the different layers in CNN?
20. What is Pooling in CNN and how does it work?
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Artificial neural network for machine learninggrinu
An Artificial Neurol Network (ANN) is a computational model. It is based on the structure and functions of biological neural networks. It works like the way human brain processes information. ANN includes a large number of connected processing units that work together to process information. They also generate meaningful results from it.
Artificial Neural Networks (ANNS) For Prediction of California Bearing Ratio ...IJMER
The behaviour of soil at the location of the project and interactions of the earth materials during and after construction has a major influence on the success, economy and safety of the work. Another complexity associated with some geotechnical engineering materials, such as sand and gravel, is the difficulty in obtaining undisturbed samples and time consuming involving skilled
technician. Knowledge of California Bearing Ratio (C.B.R) is essential in finding the road thickness. To cope up with the difficulties involved, an attempt has been made to model C.B.R in terms of Fine Fraction, Liquid Limit, Plasticity Index, Maximum Dry density, and Optimum Moisture content. A multi-layer perceptron network with feed forward back propagation is used to model varying the
number of hidden layers. For this purposes 50 soils test data was collected from the laboratory test
results. Among the test data 30 soils data is used for training and remaining 20 soils for testing using
60-40 distribution. The architectures developed are 5-4-1, 5-5-1, and 5-6-1. Model with 5-6-1 architecture is found to be quite satisfactory in predicting C.B.R of soils. A graph is plotted between
the predicted values and observed values of outputs for training and testing process, from the graph it
is found that all the points are close to equality line, indicating predicted values are close to observed
values
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
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This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Opendatabay - Open Data Marketplace.pptxOpendatabay
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2. Agenda
u Introduction
u Data Mining Techniques
u Neural Networks for Data Mining?
Neural Networks Classification
Neural Networks Pruning
Neural Networks Rule Extraction
u Conclusion
u Questions?
3. Extraction of interesting (non-trivial,
implicit, previously unknown and
potentially useful) information or patterns
from data in large databases
It is an essential step in the process of
knowledge discovery.
Data Mining
4. • data cleaning
• data integration
• data selection
• data transformation
• data mining
• pattern evaluation
• knowledge presentation.
Steps of Knowledge Discovery
5. Data Mining: A KDD Process
Data mining—core of
knowledge discovery
process
Data Cleaning
Data Integration
Databases
Data Warehouse
Task-relevant Data
Selection
Data Mining
Pattern Evaluation
6. Why Data mining?
Data explosion problem
Automated data collection tools and
mature database technology lead to
tremendous amounts of data stored in
databases, data warehouses and other
information repositories
We are drowning in data, but starving for
knowledge!
Solution: Data warehousing and data mining
7. Tasks of data mining
Concept Description
Association
Classification
Prediction
Cluster Analysis
Outlier Analysis
8. Classification
It is the process of finding a model
that is able to predict the class of
objects whose label is unknown.
For eg. It can classify the customers who can pay the
loan based on the existing records in the bank database.
9. Decision trees
Bayesian classification
Neural networks
Genetic algorithms
Memory Based Reasoning
etc.,
Classification methods
10. high tolerance of noisy data.
ability to classify patterns on which they
have not been trained.
can be used when there is little
knowledge of the relationships between
attribute and classes.
Why Neural networks?
11. well suited for continuous valued inputs
and outputs unlike most decision tree
algorithms.
rules can be extracted easily by available
techniques from trained neural network.
Why Neural networks?
- Contd.
12. Neural Networks
It is the study of how to make
computers to make sensible
decisions and to learn by ordinary
experience as we do.
13. Neurons
The human brain consists has about 100 billion neurons and
100 trillion connections (synapses) between them.
Here is what a typical neuron looks like:
Many highly specialized types of neurons exist, and these
differ widely in appearance. Characteristically, neurons are
highly asymmetric in shape.
14. It consists of an input layer, one or more
hidden layers and an output layer.
Input Layer
Hidden Layer
Output Layer
Structure of Multi layer feed forward neural network
Multi layer feed forward neural network
15. Backpropagation
Backpropagation is the neural network
learning algorithm.
It learns by iteratively processing a
dataset of training examples, comparing the
network's prediction for each example with
the actual known target value.
16. Overview of BP
The backpropagation algorithm learns
the network by iteratively processing the
np training examples of a dataset,
comparing the networks result ok for
each example with the desired known
target value dk for each target class k in
a dataset.
17. Consider a fully connected three layer
feedforward neural network as in figure ,
X1
X2
Xi
Xl
…
…
…
h1
O1
…
w11
w12
wl1
wlm
hm
On
v11
v12
vm1
vmn
Overviewof BP – Contd.
Bias (-1)
Bias (-1)
h2
18. Consists of l input neurons, m hidden
neurons and n output neurons
np be the number of examples consider
for training.
Let xip be the ith input unit of pth
example in a dataset, where i= 1, 2,… l.
Wij be the weight between input unit
neuron i to hidden unit neuron j, where
j=1,2…m,
Overview of BP – Contd.
19. vjk be the weight between hidden neuron
j to output neuron k, where k=1, 2,… n.
initially the weights wij and vjk takes the
random value between -1 to 1.
Let hj be the activation value of the
hidden neuron j
ok be the actual output of the kth neuron.
Overview of BP – Contd.
20. Bias
• It is a threshold value that serves to
vary the activity of the neuron.
• The bias input is fixed and always
equals -1.
Overview of BP – Contd.
21. The activation value of hidden neuron
hj for pth examples can be calculated by,
Overview of BP – Contd.
23. Weights are modified for each example
so as to minimize the mean squared
error (mse).
The value of mse can be calculated
according to the following equation
Overview of BP – Contd.
24. Weight updation are made in the
backward direction i.e., from the
output layer through hidden layer
and to input layer.
Overview of BP – Contd.
25. Learning Rate λ
avoids local minimum (where the
weights appear to converge but are not
at the optimal solution).
encourages finding global minimum.
Typically having a value between 0.0 to
1.0.
Overview of BP – Contd.
26. For each unit k in the output layer
compute the Error using
Errk = ok(1-ok)(dk-ok)
For each weight vjk in network
compute weight increment using
Δvjk=(λ) Errk*hj
update the weight vjk using
vjk = vjk + Δvjk
Overview of BP – Contd.
27. For each unit j in the hidden layers, from
the last to the first hidden layer
compute the Error using
Errj = hj (1-hj) Σ Errk*vjk;
For each weight wij in network
compute weight increment using
Δwij=(λ)*Errj*xip
update the weight wij using
wij = wij + Δwij
Overview of BP – Contd.
28. Overview of BP – Contd.
For each bias Ǿj in network
compute the bias increment
using
Δ Ǿj = (λ)*Errj
update the bias weight using
Ǿj = Ǿj + Δ Ǿj
29. The algorithm stops the learning when,
• The mean squared error is below a
threshold value.
• A pre specified number of epochs has
expired
Overview of BP – Contd.
30. Random data selection method
The training and testing examples are
taken randomly from each class.
K-fold cross validation method
Example
The iris dataset is having 3 classes with
50 examples for each class. From each
class 25 examples are taken randomly for
training and another 25 examples are
taken randomly for testing the network.
Data selection method.
31. Performance Measures
Accuracy
It is the percentage of test dataset
that are correctly classified by the
classifier.
Speed
It refers to computational time and
cost involved in generating and using
given classifier.
32. Evolving Network Architectures
The success of ANNs largely depends on their
architecture.
Small networks require long training time and can
be easily get trapped into a Local Minima.
Large networks able to learn fast and avoids local
minima but with poor generalization.
Optimal architecture is a network that is large
enough to learn the problem and is small enough
to generalize well.
33. approaches for optimizing Neural
Networks
Constructive methods
- new hidden units are added during the training
process, also called as Growing methods.
Destructive methods
- a large network is trained and then unimportant
nodes or weights are removed, also called as Pruning
methods.
Hybrid methods
- can both add and remove.
34. Pruning is defined as a network trimming within the
assumed initial architecture.
This can be accomplished by estimating the sensitivity
of the total error to the exclusion of each weight or
neuron in the network.
The weights or neurons which are insensitive to error
changes can be discarded after each step of training.
The trimmed network is of smaller size and is likely
give higher accuracy than before its trimming.
What is Pruning ?
35. Hepatitis Pruning Results
Step Current
Architecture
Acctest % Epochs Pruned Neurons
1 19-25-2 78.2 200 18 hidden neurons
2 19-7-2 80.5 50 5 hidden neurons
3 19-2-2 83.95 50 Pruning stops
Original network with architecture 19-25-2 with accuracy
78.2% is reduced to the architecture 19-2-2.
Requires 0.76 seconds to obtain the pruned network.
36. Rule Extraction
Why Rule extraction?
An important drawback of neural networks is their lack of
explanation capability i.e., it is very difficult to understand how
an ANN has solved a problem. To overcome this problem
various rule extraction algorithms have been developed.
Rule extraction : It changes a black box system into a white
box system by translating the internal knowledge of a neural
network into a set of symbolic rules .
The classification process of a neural networks can be
described by a set of simple rules.
38. •robots that can see, feel, and predict the world around them
•improved stock prediction
•common usage of self-driving cars
•composition of music
•handwritten documents to be automatically transformed into
formatted word processing documents
•trends found in the human genome to aid in the
understanding of the data compiled by the Human Genome
Project
•self-diagnosis of medical problems using neural networks
and much more!
NNs might, in the future, allow: