image classification is a common problem in Artificial Intelligence , we used CIFR10 data set and tried a lot of methods to reach a high test accuracy like neural networks and Transfer learning techniques .
you can view the source code and the papers we read on github : https://github.com/Asma-Hawari/Machine-Learning-Project-
classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects.we'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded.
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.
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).
image classification is a common problem in Artificial Intelligence , we used CIFR10 data set and tried a lot of methods to reach a high test accuracy like neural networks and Transfer learning techniques .
you can view the source code and the papers we read on github : https://github.com/Asma-Hawari/Machine-Learning-Project-
classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects.we'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded.
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.
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.
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.
Learn the fundamentals of Deep Learning, Machine Learning, and AI, how they've impacted everyday technology, and what's coming next in Artificial Intelligence technology.
In this presentation we discuss the convolution operation, the architecture of a convolution neural network, different layers such as pooling etc. This presentation draws heavily from A Karpathy's Stanford Course CS 231n
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.
Convolutional Neural Network - CNN | How CNN Works | Deep Learning Course | S...Simplilearn
This presentation on Convolutional neural network tutorial (CNN) will help you understand what is a convolutional neural network, hoe CNN recognizes images, what are layers in the convolutional neural network and at the end, you will see a use case implementation using CNN. CNN is a feed forward neural network that is generally used to analyze visual images by processing data with grid like topology. A CNN is also known as a "ConvNet". Convolutional networks can also perform optical character recognition to digitize text and make natural-language processing possible on analog and hand-written documents. CNNs can also be applied to sound when it is represented visually as a spectrogram. Now, lets deep dive into this presentation to understand what is CNN and how do they actually work.
Below topics are explained in this CNN presentation(Convolutional Neural Network presentation)
1. Introduction to CNN
2. What is a convolutional neural network?
3. How CNN recognizes images?
4. Layers in convolutional neural network
5. Use case implementation using CNN
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. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
Learn more at: https://www.simplilearn.com/
Unveiling the Power of Convolutional Neural Networks in Image Processing.pdfEnterprise Wired
In this comprehensive guide, we'll explore the significance of convolutional neural networks, delve into their architecture and functioning, and highlight their transformative impact on image processing and beyond.
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.
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.
Learn the fundamentals of Deep Learning, Machine Learning, and AI, how they've impacted everyday technology, and what's coming next in Artificial Intelligence technology.
In this presentation we discuss the convolution operation, the architecture of a convolution neural network, different layers such as pooling etc. This presentation draws heavily from A Karpathy's Stanford Course CS 231n
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.
Convolutional Neural Network - CNN | How CNN Works | Deep Learning Course | S...Simplilearn
This presentation on Convolutional neural network tutorial (CNN) will help you understand what is a convolutional neural network, hoe CNN recognizes images, what are layers in the convolutional neural network and at the end, you will see a use case implementation using CNN. CNN is a feed forward neural network that is generally used to analyze visual images by processing data with grid like topology. A CNN is also known as a "ConvNet". Convolutional networks can also perform optical character recognition to digitize text and make natural-language processing possible on analog and hand-written documents. CNNs can also be applied to sound when it is represented visually as a spectrogram. Now, lets deep dive into this presentation to understand what is CNN and how do they actually work.
Below topics are explained in this CNN presentation(Convolutional Neural Network presentation)
1. Introduction to CNN
2. What is a convolutional neural network?
3. How CNN recognizes images?
4. Layers in convolutional neural network
5. Use case implementation using CNN
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. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
Learn more at: https://www.simplilearn.com/
Unveiling the Power of Convolutional Neural Networks in Image Processing.pdfEnterprise Wired
In this comprehensive guide, we'll explore the significance of convolutional neural networks, delve into their architecture and functioning, and highlight their transformative impact on image processing and beyond.
This covers a end-to-end coverage of neural networks,CNN internals , Tensorflow and Keras basic , intution on object detection and face recognition and AI on Android x86.
Scene recognition using Convolutional Neural NetworkDhirajGidde
Scene recognition is one of the hallmark tasks of computer vision, allowing definition of a context for object recognition. Whereas the tremendous recent progress in object recognition tasks is due to the availability of large datasets like ImageNet and the rise of Convolutional Neural Networks (CNNs) for learning high-level features, performance at scene recognition has not attained the same level of success.
In the realm of artificial intelligence and machine learning, the Convolutional Neural Network (CNN) is a powerful tool. They're like computer superheroes, assisting computers in understanding and recognizing patterns in images. This article will explain what CNNs are, how they work, and why they are so important in today's technology scene.
What is a Convolutional Neural Network?
Convolutional Neural Networks are computer programs that learn from images. Consider it a deft detective who can find minute elements in a photograph, such as edges, contours, or even individual traits. CNNs are built to process visual input, making them ideal for image recognition and classification.
The Basic Structure of a Convolutional Neural Network (CNN)
A CNN is made up of layers that operate together as a team. These layers assist the network in gradually learning the main aspects of a picture.
1. Input Layer
This is the location where the CNN receives the image to be evaluated. The input layer functions as a stage, with the image serving as the main performer.
2. Convolutional Layers
A CNN's heart is made up of these layers. Filters, which are tiny grids used to scan the input image, are included. Patterns such as edges or textures are highlighted by the filters.
3. Activation Layers
Activation layers give a touch of magic after convolution. They introduce non-linearity into the image, allowing the CNN to discern complex patterns and variations.
4. Pooling Layers
Pooling Layering information simplifies it. They compress the data while retaining the key qualities. It's similar to condensing a large story into a few vital elements.
5. Fully Connected Layers
The network connects all of the features it has learned in these layers to reach a final conclusion. It's similar to assembling all of the evidence to solve a mystery.
How a Convolutional Neural Network (CNN) Works
CNNs learn by observing examples. They examine thousands of photos to determine what distinguishes a cat from a dog. It's similar to studying a large number of photographs of various animals in order to identify unique qualities.
Learning Patterns: The CNN begins by inspecting the images and learning various patterns such as colors, shapes, and textures.
Feature Extraction: The convolutional layers' filters then highlight these patterns. Consider a flashlight that illuminates specific areas of the image.
Recognizing Complex Patterns: The activation layers assist the CNN in combining these patterns in order to recognize more complex features such as eyes, noses, and tails.
Decision Making: The fully connected layers then analyze all of these features and decide whether the image is of a cat or a dog.
Applications of CNNs
Because of their exceptional image processing capabilities, Convolutional Neural Networks have a wide range of applications. Here are a few examples of key areas where CNNs are having a significant impact:
1. Image Recognition
Automatic Attendace using convolutional neural network Face Recognitionvatsal199567
Automatic Attendance System will recognize the face of the student through the camera in the class and mark the attendance. It was built in Python with Machine Learning.
Space robotics with examples , this ppt contains introduction of robot , structure of robot ,why space robot is necessary, challenges of space robots ,advantages & disadvantages of robots with example ..created by Sumera Hangi
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
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
2. CONTENTS
– INTRODUCTION (core subject)
– INTRODUCTION TO CONVOLUTIONAL NEURAL NETWORKS
– WHY DID WE USE A CNN?
– STEPS TO DESIGN CNN
– KEY DIFFERENCE BETWEEN ANN & CNN
– ARCHITECTURE OF A CNN FROM INPUT IMAGE TO IMAGE
REGONITION
– APPLICATIONS OF CNN
– CONCLUSION
3. INTRODUCTION (core subject)
– IMAGE PROCESSING is a method to perform some operation on image, in
order to get an enhanced image or extract some useful information from it.
– COMPUTER VISION is a field of computer science that works on enabling
computer to see, identify & process images in the same way that human vision
does, and then provide appropriate output.
– IMAGE CLASSIFICATION is a complex process that may affected by many
factors.
– The objective of image classification is to identify and portray, as a unique gray
level(or color), the feature occurring in an image in terms of the object or type of
land cover these feature actually represent on the ground .
– Image classification is perhaps the most important part of digital image analysis.
4. INTRODUCTION TO CONVOLUTIONAL
NEURAL NETWORKS
– CNNs are basically combination of convolutions followed by feature maps,
subsampling, image features, dense layer of neural network .
– We are using convolutions ,subsampling , feature maps to extract image feature &
dense layer of NN to categorize input image according to image features.
– Applications of CNN are image & video recognition, object recognition, natural
language processing.
5.
6. SO, WHY DID WE USE A CNN?
– In machine learning, a convolutional neural network is a class of deep, feed-
forward artificial neural networks that has successfully been applied to analyzing
visual imagery.
– CNNs use a variation of multilayer perceptron's designed to require minimal
preprocessing. They are also known as shift invariant or space invariant artificial
neural networks (SIANN), based on their shared-weights architecture and
translation invariance characteristics.
7. STEPS TO DESIGN CNN
– Convolution
– Max pooling
– Flattening
– Full Connection
8. CONVOLUTION
– A convolution sweeps the window through images then calculates its input and filter dot product pixel
values. This allows convolution to emphasize the relevant features.
11. RELU
ReLu is a non-linear activation
function that is used in multi-
layer neural networks or deep
neural networks.
This function can be represented
as: where x = an input value
According to equation 1, the
output of ReLu is the maximum
value between zero and the
input value.
We are using Relu on the
convolution to increase non
linearity
12. MAX
POOLING
CNN uses max pooling to
replace output with a max
summary to reduce data size and
processing time.
This allows you to determine
features that produce the highest
impact and reduces the size of
the image by 75% to avoid the
risk of overfitting also get image
extracted features.
14. FULL CONNECTION
– Neurons of hidden neural network layers are fully connected with all the features
– Activation function determines the value of the output layer
Softmax
• We use softmax activation function in the output layer of multiclass CNN system.
• It uses multiple classification logistic regression model.
• It calculates the probability distribution of the event over all the different events.
• The range of the output of softmax is 0 to 1 & the sum of all the probabilities will be
equal to one
16. KEY DIFFERENCE BETWEEN
ANN & CNN
– In CNN, neuron of hidden neural network are fully connected .
– In CNN , input feature are connected with all the neurons. But , in ANN , input
features are connected with only those neurons that are having similar values or
pattern.
21. CONCLUSION
– Convolutional neural networks (CNNs) have accomplished astonishing
achievements across a variety of domains, including medical research, and an
increasing interest has emerged in radiology.
– Although deep learning has become a dominant method in a variety of complex
tasks such as image classification and object detection, it is not a panacea.
– Being familiar with key concepts and advantages of CNN as well as limitations
of deep learning is essential in order to leverage it in radiology research with the
goal of improving radiologist performance and, eventually, patient care.