Here is a Python program to train and simulate a neural network with 2 input nodes, 1 hidden layer with 3 nodes, and 1 output node to perform an XOR operation:
```python
import numpy as np
# Network parameters
num_input = 2 # Input nodes
num_hidden = 3 # Hidden layer nodes
num_output = 1 # Output node
# Training data
X = np.array([[0,0], [0,1], [1,0], [1,1]])
y = np.array([[0], [1], [1], [0]])
# Initialize weights randomly with mean 0
hidden_weights = 2*np.random.random((num_
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.
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.
Image classification using convolutional neural networkKIRAN R
For separating the images from a large collection of images or from a large dataset this classifier can be used, Here deep neural network is used for training and classifying the images. The convolutional neural network is the most suitable algorithm for classifier images. This Classifier is a machine learning model, so the more you train it the more will be the accuracy.
This presentation provides an introduction to the artificial neural networks topic, its learning, network architecture, back propagation training algorithm, and its applications.
In machine learning, a convolutional neural network is a class of deep, feed-forward artificial neural networks that have successfully been applied fpr analyzing visual imagery.
This is a presentation on Handwritten Digit Recognition using Convolutional Neural Networks. Convolutional Neural Networks give better results as compared to conventional Artificial Neural Networks.
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.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
An artificial neuron network (ANN) is a computational model based on the structure and functions of biological neural networks. It works on real-valued, discrete-valued and vector valued.
Image classification using convolutional neural networkKIRAN R
For separating the images from a large collection of images or from a large dataset this classifier can be used, Here deep neural network is used for training and classifying the images. The convolutional neural network is the most suitable algorithm for classifier images. This Classifier is a machine learning model, so the more you train it the more will be the accuracy.
This presentation provides an introduction to the artificial neural networks topic, its learning, network architecture, back propagation training algorithm, and its applications.
In machine learning, a convolutional neural network is a class of deep, feed-forward artificial neural networks that have successfully been applied fpr analyzing visual imagery.
This is a presentation on Handwritten Digit Recognition using Convolutional Neural Networks. Convolutional Neural Networks give better results as compared to conventional Artificial Neural Networks.
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.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
An artificial neuron network (ANN) is a computational model based on the structure and functions of biological neural networks. It works on real-valued, discrete-valued and vector valued.
Contains description of CPN.
CP algorithm consists of a input, hidden and output layer.
In this case the hidden layer is called the Kohonen layer & the output layer is called the Grossberg layer.
Neural networks Self Organizing Map by Engr. Edgar Carrillo IIEdgar Carrillo
This presentation talks about neural networks and self organizing maps. In this presentation,Engr. Edgar Caburatan Carrillo II also discusses its applications.
Self-Organising Maps for Customer Segmentation using R - Shane Lynn - Dublin Rshanelynn
Self-Organising maps for Customer Segmentation using R.
These slides are from a talk given to the Dublin R Users group on 20th January 2014. The slides describe the uses of customer segmentation, the algorithm behind Self-Organising Maps (SOMs) and go through two use cases, with example code in R.
Accompanying code and datasets now available at http://shanelynn.ie/index.php/self-organising-maps-for-customer-segmentation-using-r/.
Classification by back propagation, multi layered feed forward neural network...bihira aggrey
Classification by Back Propagation, Multi-layered feed forward Neural Networks - Provides a basic introduction of classification in data mining with neural networks
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?
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
Learn more at: https//www.simplilearn.com
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Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Online aptitude test management system project report.pdfKamal Acharya
The purpose of on-line aptitude test system is to take online test in an efficient manner and no time wasting for checking the paper. The main objective of on-line aptitude test system is to efficiently evaluate the candidate thoroughly through a fully automated system that not only saves lot of time but also gives fast results. For students they give papers according to their convenience and time and there is no need of using extra thing like paper, pen etc. This can be used in educational institutions as well as in corporate world. Can be used anywhere any time as it is a web based application (user Location doesn’t matter). No restriction that examiner has to be present when the candidate takes the test.
Every time when lecturers/professors need to conduct examinations they have to sit down think about the questions and then create a whole new set of questions for each and every exam. In some cases the professor may want to give an open book online exam that is the student can take the exam any time anywhere, but the student might have to answer the questions in a limited time period. The professor may want to change the sequence of questions for every student. The problem that a student has is whenever a date for the exam is declared the student has to take it and there is no way he can take it at some other time. This project will create an interface for the examiner to create and store questions in a repository. It will also create an interface for the student to take examinations at his convenience and the questions and/or exams may be timed. Thereby creating an application which can be used by examiners and examinee’s simultaneously.
Examination System is very useful for Teachers/Professors. As in the teaching profession, you are responsible for writing question papers. In the conventional method, you write the question paper on paper, keep question papers separate from answers and all this information you have to keep in a locker to avoid unauthorized access. Using the Examination System you can create a question paper and everything will be written to a single exam file in encrypted format. You can set the General and Administrator password to avoid unauthorized access to your question paper. Every time you start the examination, the program shuffles all the questions and selects them randomly from the database, which reduces the chances of memorizing the questions.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
2. Basic Neuron Model In A
Feedforward Network
• Inputs xi
arrive through
pre-synaptic connections
• Synaptic efficacy is
modeled using real
weights wi
• The response of the
neuron is a nonlinear
function f of its weighted
inputs
3.
4. Task
Plot the following type of Neural activation functions.
1(a) Threshold Function
φ(v)= +1 for v≥0
0 for v<0
1(b) Threshold Function
φ(v)= +1 for v≥0
-1 otherwise
2 Piecewise linear Function
φ(v)= 1 for v≥+1/2
v for +1/2>v>-1/2
0 for v≤-1/2
3(a) Sigmoid Function
φ(v)=1/(1+ exp(-λv))
3(b) Sigmoid Function
φ(v)=2/(1+ exp(-λv))
3(c) Sigmoid Function
φ(v)=tanh(λv)
For 3 vary the value of ‘λ’ and show the changes in the graph.
15. 1970s
The Backpropagation algorithm was first proposed by
Paul Werbos in the 1970's. However, it was
rediscoved in 1986 by Rumelhart and McClelland &
became widely used.
It took 30 years before the error backpropagation (or
in short: backprop) algorithm popularized.
16.
17. Differences In Networks
Feedforward Networks
• Solutions are known
• Weights are learned
• Evolves in the weight
space
• Used for:
– Prediction
– Classification
– Function
approximation
Feedback Networks
• Solutions are
unknown
• Weights are
prescribed
• Evolves in the state
space
• Used for:
– Constraint satisfaction
– Optimization
– Feature matching
18. Architecture
A Back Prop network has atleast 3 layers of units:
an input layer, at least one intermediate hidden layer, &
an output layer. Connection weights in a Back Prop
network are one way. Units are connected in a feed-
forward fashion with input units fully connected to units
in the hidden layer & hidden units fully connected to units
in the output layer. When a Back Prop network is cycled,
an input pattern is propagated forward to the output units
through the intervening input-to-hidden and hidden-to-
output weights.
19. Inputs To Neurons
• Arise from other neurons or from outside
the network
• Nodes whose inputs arise outside the
network are called input nodes and simply
copy values
• An input may excite or inhibit the response
of the neuron to which it is applied,
depending upon the weight of the
connection
21. Weights
• Represent synaptic efficacy and may be
excitatory or inhibitory
• Normally, positive weights are considered
as excitatory while negative weights are
thought of as inhibitory
• Learning is the process of modifying the
weights in order to produce a network that
performs some function
26. Backpropagation Preparation
• Training Set
A collection of input-output patterns that are
used to train the network
• Testing Set
A collection of input-output patterns that are
used to assess network performance
• Learning Rate-η
A scalar parameter, analogous to step size in
numerical integration, used to set the rate of
adjustments
27. Learning
• Learning occurs during a training phase in which each input
pattern in a training set is applied to the input units and then
propagated forward.
• The pattern of activation arriving at the output layer is then
compared with the correct output pattern to calculate an
error signal.
• The error signal for each such target output pattern is then
back propagated from the outputs to the inputs in order to
appropriately adjust the weights in each layer of the network.
28. Learning
• The process goes on for several cycles till the error
reduces to a predefined limit.
• After a BackProp network has learned the correct
classification for a set of inputs, it can be tested on a
second set of inputs to see how well it classifies
untrained patterns.
• Thus, an important consideration in applying
BackProp learning is how well the network
generalizes.
29. The basic principles of the back propagation algorithm are:
(1) the error of the output signal of a neuron is used to
adjust its weights such that the error decreases, and (2)
the error in hidden layers is estimated proportional to the
weighted sum of the (estimated) errors in the layer
above.
31. During the training, the data is presented to the network
several thousand times. For each data sample, the
current output of the network is calculated and compared
to the "true" target value. The error signal dj of neuron j
is computed from the difference between the target and
the calculated output. For hidden neurons, this difference
is estimated by the weighted error signals of the layer
above. The error terms are then used to adjust the
weights wij of the neural network.
32. A Pseudo-Code Algorithm
• Randomly choose the initial weights
• While error is too large
– For each training pattern (presented in random order)
• Apply the inputs to the network
• Calculate the output for every neuron from the input layer,
through the hidden layer(s), to the output layer
• Calculate the error at the outputs
• Use the output error to compute error signals for pre-output
layers
• Use the error signals to compute weight adjustments
• Apply the weight adjustments
– Periodically evaluate the network performance
35. Apply Inputs From A Pattern
• Apply the value of
each input parameter
to each input node
• Input nodes computer
only the identity
function
Feedforward
Inputs
Outputs
36. Calculate Outputs For Each
Neuron Based On The Pattern
• The output from neuron j
for pattern p is Opj where
and
k ranges over the input
indices and Wjk is the
weight on the connection
from input k to neuron j
Feedforward
Inputs
Outputs
jnetjpj
e
netO λ−
+
=
1
1
)(
∑+=
k
jkpkbiasj WOWbiasnet *
37. Calculate The Error Signal For
Each Output Neuron
• The output neuron error signal δpj is given
by δpj=(Tpj-Opj) Opj (1-Opj)
• Tpj is the target value of output neuron j for
pattern p
• Opj is the actual output value of output
neuron j for pattern p
38. Calculate The Error Signal For
Each Hidden Neuron
• The hidden neuron error signal δpj is given
by
where δpk is the error signal of a post-
synaptic neuron k and Wkj is the weight of
the connection from hidden neuron j to the
post-synaptic neuron k
kj
k
pkpjpjpj WOO ∑−= δδ )1(
39. Calculate And Apply Weight
Adjustments
• Compute weight adjustments ∆Wji at time
t by
∆Wji(t)= η δpj Opi
• Apply weight adjustments according to
Wji(t+1) = Wji(t) + ∆Wji(t)
• Some add a momentum term α∗∆Wji(t-1)
40. • Thus, the network adjusts its weights after each data
sample. This learning process is in fact a gradient
descent in the error surface of the weight space - with all
its drawbacks. The learning algorithm is slow and prone
to getting stuck in a local minimum.
41.
42. Simulation Issues
How to Select Initial Weights
Local Minima
Solutions to Local minima
Rate of Learning
Stopping Criterion
Initialization
43. • For the standard back propagation algorithm, the initial
weights of the multi-layer perceptron have to be
relatively small. They can, for instance, be selected
randomly from a small interval around zero. During
training they are slowly adapted. Starting with small
weights is crucial, because large weights are rigid and
cannot be changed quickly.
44. Sequential & Batch modes
For a given training set ,back-propagation learning
proceeds in two basic ways:
1. Sequential Mode
2. Batch Mode
45. Sequential mode
• The sequential mode of back-propagation learning is also
referred to as on-line, pattern or stochastic mode.
• To be specific, consider an epoch consisting of N training ex.
Arranged in the order (x(1),d(1)),…,(x(N),d(N)).
• The first ex. pair (x(1),d(1))in the epoch is presented to the
network,& the sequence of forward & backward computations
described previously is performed, resulting in certain adjustments
to the synaptic weights & bias level of the network.
• The second ex. (x(N),d(N)) in the epoch is presented,& the
sequence of forward & backward computations is repeated,
resulting in the further adjustments to the synaptic weights & bias
levels. This process is continued until the last example pair
(x(N),d(N)) in the epoch is accounted for.
46. Batch Propagation
• In this mode of back-propagation learning weight
updating is performed after the presentation of all the
training examples that constitute an epoch.
• For a particular epoch, the cost function is the average
squared error, reproduced here in composite form is
defined as:-
ξav = (1/2N )Σ Σ ej
2
(n) for n=1 to N
for j € C
47. • Let N denote the total no. of patterns contained in the
training set. The average squared error energy is
obtained by summing ξ(n) over all n and then
normalizing with respect to the set size N, as shown by :-
• ξav = 1/N Σ ξ(n) for n=1 to N
48. Stopping Criteria
• The back-propagation algorithm cannot be shown to converge .
• To formulate a criterion, it is logically to think in terms of the
unique properties of a local or global minimum.
• The back-propagation algorithm is considered to have
converged when the Euclidean norm of the gradient vector reaches
a sufficient small gradient threshold.
• The back-propagation algorithm is considered to have converged
when the absolute rate of change in the average squared error pre
epoch is sufficiently small.
• The drawback of this convergence criterion is that, for
successful trials, learning time may be long.
49.
50. • The back-propagation algorithm makes adjustments by
computing the derivative, or slope of the network error
with respect to each neuron’s output. It attempts to
minimize the overall error by descending this slope to the
minimum value for every weight. It advances one step
down the slope each epoch. If the network takes steps
that are too large, it may pass the global minimum. If it
takes steps that are small, it may settle on local minima,
or take an inordinate amount of time to arrive at the
global minimum. The ideal step size for a given problem
requires detailed, high-order derivative analysis, a task
not performed by the algorithm.
53. Local Minima
For simple 2 layer networks (without a hidden layer), the
error surface is bowl shaped and using gradient-descent to
minimize error is not a problem; the network will always
find an errorless solution (at the bottom of the bowl). Such
errorless solutions are called global minima.
However, extra hidden layer implies complex surfaces.
Since some minima are deeper than others, it is possible
that gradient descent may not find a global minima.
Instead, the network may fall into local minima which
represent suboptimal solutions.
54. • The algorithm cycles through the training samples as:-
• Initialization
• Presentation of training Examples
• Forward Computation
55. Initialization
• Assuming that no prior information is available, pick the
synaptic weights and thresholds from a uniform
distribution whose mean is zero & whose variance is
chosen to make the standard deviation of the induced
local fields of the neurons lie at the transition between
the linear and saturated parts of the sigmoid activation
function.
56. Presentation of training Examples
Present the network with an epoch of training examples.
For each example in the set order in same fashion,
perform the sequence of forward and backward
computation as described below.
57. Solutions to Local minima
Usual solution : More hidden layers. Logic -
Although additional hidden units increase the
complexity of the error surface, the extra
dimensionalilty increases the number of possible
escape routes.
Our solution – Tunneling
58. Rate of Learning
If the learning rate η is very small, then the
algorithm proceeds slowly, but accurately follows
the path of steepest descent in weight space.
If η is large, the algorithm may oscillate.
59. A simple method of effectively increasing the rate of
learning is to modify the delta rule by including a
momentum term:
Δwji
(n) = α Δwji
(n-1) + η δj
(n)yi
(n)
where α is a positive constant termed the momentum
constant. This is called the generalized delta rule.
The effect is that if the basic delta rule is consistently
pushing a weight in the same direction, then it gradually
gathers "momentum" in that direction.
61. An Example: Exclusive “OR”
• Training set
– ((0.1, 0.1), 0.1)
– ((0.1, 0.9), 0.9)
– ((0.9, 0.1), 0.9)
– ((0.9, 0.9), 0.1)
• Testing set
– Use at least 121 pairs equally spaced on the
unit square and plot the results
– Omit the training set (if desired)
64. Feedforward Network Training by
Backpropagation: Process
Summary
• Select an architecture
• Randomly initialize weights
• While error is too large
– Select training pattern and feedforward to find
actual network output
– Calculate errors and backpropagate error
signals
– Adjust weights
• Evaluate performance using the test set
65. An Example (continued):
Network Architecture
Sample
input
0.1
0.9
Actual
output
???
1
1
1
??
??
??
??
??
??
??
??
??
Target
output
0.9
66. Feedforward Network Training by
Backpropagation: Process
Summary
• Select an architecture
• Randomly initialize weights
• While error is too large
– Select training pattern and feedforward to find
actual network output
– Calculate errors and backpropagate error
signals
– Adjust weights
• Evaluate performance using the test set
67. Backpropagation
•Very powerful - can learn any function, given enough
hidden units! With enough hidden units, we can
generate any function.
•Have the same problems of Generalization vs.
Memorization. With too many units, we will tend to
memorize the input and not generalize well. Some
schemes exist to “prune” the neural network.
68. BackProp networks are not limited in its use because
they can adapt their weights to acquire new
knowledge. BackProp networks learn by example,
and can be used to make predictions.
69. Write a program to train and simulate neural
network for following network
– Input Nodes = 2 &
Output Nodes = 1
– Input Nodes = 3 and
Output nodes = 1
Inputs Outputs
A B Y
0 0 0
0 1 1
1 0 1
1 1 0
Inputs Outputs
A B C Y
0 0 0 0
0 0 1 0
0 1 0 0
0 1 1 0
1 0 0 1
1 0 1 1
1 1 0 1
1 1 1 1
70. • Artificial Neural Network
– Simon Haykin
• Artificial Neural Network
– Jacek Zurada