�ݺ�ߣshows by User: AhmedGadFCIT

�ݺ�ߣshows by User: AhmedGadFCIT / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: AhmedGadFCIT / Sat, 18 Jul 2020 17:20:26 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: AhmedGadFCIT ICEIT'20 Cython for Speeding-up Genetic Algorithm /slideshow/iceit20-cython-for-speedingup-genetic-algorithm/237030502 iceitahmedgadfatimaezzahracython-200718172026
The presentation of the paper titled "Cython for Speeding-up Genetic Algorithm". Find it at IEEE Explore: https://ieeexplore.ieee.org/document/9113210 The abstract of the paper: This paper proposes a library for implementing the genetic algorithm using Python mainly in NumPy and speeding-up its execution using Cython. The preliminary Python implementation is inspected for possible optimizations. The 4 main changes include statically defining data types for the NumPy arrays, specifying the data type of the array elements in addition to the number of dimensions, using indexing for looping through the arrays, and finally disabling some unnecessary features in Cython. Using Cython, the NumPy array processing is 1250 times faster than CPython. The Cythonized version of the genetic algorithm is 18 times faster than the Python version.]]>
The presentation of the paper titled "Cython for Speeding-up Genetic Algorithm". Find it at IEEE Explore: https://ieeexplore.ieee.org/document/9113210 The abstract of the paper: This paper proposes a library for implementing the genetic algorithm using Python mainly in NumPy and speeding-up its execution using Cython. The preliminary Python implementation is inspected for possible optimizations. The 4 main changes include statically defining data types for the NumPy arrays, specifying the data type of the array elements in addition to the number of dimensions, using indexing for looping through the arrays, and finally disabling some unnecessary features in Cython. Using Cython, the NumPy array processing is 1250 times faster than CPython. The Cythonized version of the genetic algorithm is 18 times faster than the Python version.]]> Sat, 18 Jul 2020 17:20:26 GMT /slideshow/iceit20-cython-for-speedingup-genetic-algorithm/237030502 AhmedGadFCIT@slideshare.net(AhmedGadFCIT) ICEIT'20 Cython for Speeding-up Genetic Algorithm AhmedGadFCIT The presentation of the paper titled "Cython for Speeding-up Genetic Algorithm". Find it at IEEE Explore: https://ieeexplore.ieee.org/document/9113210 The abstract of the paper: This paper proposes a library for implementing the genetic algorithm using Python mainly in NumPy and speeding-up its execution using Cython. The preliminary Python implementation is inspected for possible optimizations. The 4 main changes include statically defining data types for the NumPy arrays, specifying the data type of the array elements in addition to the number of dimensions, using indexing for looping through the arrays, and finally disabling some unnecessary features in Cython. Using Cython, the NumPy array processing is 1250 times faster than CPython. The Cythonized version of the genetic algorithm is 18 times faster than the Python version. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/iceitahmedgadfatimaezzahracython-200718172026-thumbnail.jpg?width=120&height=120&fit=bounds" /> The presentation of the paper titled "Cython for Speeding-up Genetic Algorithm". Find it at IEEE Explore: https://ieeexplore.ieee.org/document/9113210 The abstract of the paper: This paper proposes a library for implementing the genetic algorithm using Python mainly in NumPy and speeding-up its execution using Cython. The preliminary Python implementation is inspected for possible optimizations. The 4 main changes include statically defining data types for the NumPy arrays, specifying the data type of the array elements in addition to the number of dimensions, using indexing for looping through the arrays, and finally disabling some unnecessary features in Cython. Using Cython, the NumPy array processing is 1250 times faster than CPython. The Cythonized version of the genetic algorithm is 18 times faster than the Python version.

ICEIT'20 Cython for Speeding-up Genetic Algorithm from Ahmed Gad

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0 NumPyCNNAndroid: A Library for Straightforward Implementation of Convolutional Neural Networks for Android Devices - ITCE 2019 /slideshow/numpycnnandroid-a-library-for-straightforward-implementation-of-convolutional-neural-networks-for-android-devices-itce-2019/130623924 ahmedgad-itce-npca-190205192615
The presentation of my paper titled "#NumPyCNNAndroid: A Library for Straightforward Implementation of #ConvolutionalNeuralNetworks for #Android Devices" at the second International Conference of Innovative Trends in #ComputerEngineering (ITCE 2019). The paper proposes a library for implementing convolutional neural networks (CNNs) in order to run on Android devices. The process of running the CNN on the mobile devices is straightforward and does not require an in-between step for model conversion as it uses #Kivy cross-platform library. The CNN layers are implemented in #NumPy. You can find their implementation in my #GitHub project at this link: https://github.com/ahmedfgad/NumPyCNN The library is also open source available here: https://github.com/ahmedfgad/NumPyCNNAndroid There are 2 modes of operation for this work. The first one is training the CNN on the mobile device but it is very time-consuming at least in the current version. The second and preferred way is to train the CNN in a desktop computer and then use it on the mobile device.]]>
The presentation of my paper titled "#NumPyCNNAndroid: A Library for Straightforward Implementation of #ConvolutionalNeuralNetworks for #Android Devices" at the second International Conference of Innovative Trends in #ComputerEngineering (ITCE 2019). The paper proposes a library for implementing convolutional neural networks (CNNs) in order to run on Android devices. The process of running the CNN on the mobile devices is straightforward and does not require an in-between step for model conversion as it uses #Kivy cross-platform library. The CNN layers are implemented in #NumPy. You can find their implementation in my #GitHub project at this link: https://github.com/ahmedfgad/NumPyCNN The library is also open source available here: https://github.com/ahmedfgad/NumPyCNNAndroid There are 2 modes of operation for this work. The first one is training the CNN on the mobile device but it is very time-consuming at least in the current version. The second and preferred way is to train the CNN in a desktop computer and then use it on the mobile device.]]> Tue, 05 Feb 2019 19:26:15 GMT /slideshow/numpycnnandroid-a-library-for-straightforward-implementation-of-convolutional-neural-networks-for-android-devices-itce-2019/130623924 AhmedGadFCIT@slideshare.net(AhmedGadFCIT) NumPyCNNAndroid: A Library for Straightforward Implementation of Convolutional Neural Networks for Android Devices - ITCE 2019 AhmedGadFCIT The presentation of my paper titled "#NumPyCNNAndroid: A Library for Straightforward Implementation of #ConvolutionalNeuralNetworks for #Android Devices" at the second International Conference of Innovative Trends in #ComputerEngineering (ITCE 2019). The paper proposes a library for implementing convolutional neural networks (CNNs) in order to run on Android devices. The process of running the CNN on the mobile devices is straightforward and does not require an in-between step for model conversion as it uses #Kivy cross-platform library. The CNN layers are implemented in #NumPy. You can find their implementation in my #GitHub project at this link: https://github.com/ahmedfgad/NumPyCNN The library is also open source available here: https://github.com/ahmedfgad/NumPyCNNAndroid There are 2 modes of operation for this work. The first one is training the CNN on the mobile device but it is very time-consuming at least in the current version. The second and preferred way is to train the CNN in a desktop computer and then use it on the mobile device. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ahmedgad-itce-npca-190205192615-thumbnail.jpg?width=120&height=120&fit=bounds" /> The presentation of my paper titled "#NumPyCNNAndroid: A Library for Straightforward Implementation of #ConvolutionalNeuralNetworks for #Android Devices" at the second International Conference of Innovative Trends in #ComputerEngineering (ITCE 2019). The paper proposes a library for implementing convolutional neural networks (CNNs) in order to run on Android devices. The process of running the CNN on the mobile devices is straightforward and does not require an in-between step for model conversion as it uses #Kivy cross-platform library. The CNN layers are implemented in #NumPy. You can find their implementation in my #GitHub project at this link: https://github.com/ahmedfgad/NumPyCNN The library is also open source available here: https://github.com/ahmedfgad/NumPyCNNAndroid There are 2 modes of operation for this work. The first one is training the CNN on the mobile device but it is very time-consuming at least in the current version. The second and preferred way is to train the CNN in a desktop computer and then use it on the mobile device.

NumPyCNNAndroid: A Library for Straightforward Implementation of Convolutional Neural Networks for Android Devices - ITCE 2019 from Ahmed Gad

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0 Python for Computer Vision - Revision 2nd Edition /AhmedGadFCIT/python-for-computer-vision-revision-2nd-edition pythonforcomputervision-180920221349
Python is a powerful tool for computer vision applications. This presentation reviews the essential libraries required for image analysis using Python. These libraries include NumPy, SciPy, Matplotlib, Python Image Library (PIL), scikit-image, and scikit-learn.]]>
Python is a powerful tool for computer vision applications. This presentation reviews the essential libraries required for image analysis using Python. These libraries include NumPy, SciPy, Matplotlib, Python Image Library (PIL), scikit-image, and scikit-learn.]]> Thu, 20 Sep 2018 22:13:49 GMT /AhmedGadFCIT/python-for-computer-vision-revision-2nd-edition AhmedGadFCIT@slideshare.net(AhmedGadFCIT) Python for Computer Vision - Revision 2nd Edition AhmedGadFCIT Python is a powerful tool for computer vision applications. This presentation reviews the essential libraries required for image analysis using Python. These libraries include NumPy, SciPy, Matplotlib, Python Image Library (PIL), scikit-image, and scikit-learn. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pythonforcomputervision-180920221349-thumbnail.jpg?width=120&height=120&fit=bounds" /> Python is a powerful tool for computer vision applications. This presentation reviews the essential libraries required for image analysis using Python. These libraries include NumPy, SciPy, Matplotlib, Python Image Library (PIL), scikit-image, and scikit-learn.

Python for Computer Vision - Revision 2nd Edition from Ahmed Gad

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0 Multi-Objective Optimization using Non-Dominated Sorting Genetic Algorithm with Numerical Example Step-by-Step /slideshow/multiobjective-optimization-using-nondominated-sorting-genetic-algorithm-with-numerical-example-stepbystep/111832756 nsga-180827224617
When solving a problem, the goal is not only solving it but also optimizing such solution. There might be multiple solutions to a problem and the challenge is to find the best of them. The more metrics defining the solution goodness, the harder finding the best solution. This presentation discusses one of the multi-objective optimization techniques called non-dominated sorting genetic algorithm II (NSGA-II) explaining its steps including non-dominated sorting, crowding distance, tournament selection, and genetic algorithm. The presentation works through a numerical example step-by-step.]]>
When solving a problem, the goal is not only solving it but also optimizing such solution. There might be multiple solutions to a problem and the challenge is to find the best of them. The more metrics defining the solution goodness, the harder finding the best solution. This presentation discusses one of the multi-objective optimization techniques called non-dominated sorting genetic algorithm II (NSGA-II) explaining its steps including non-dominated sorting, crowding distance, tournament selection, and genetic algorithm. The presentation works through a numerical example step-by-step.]]> Mon, 27 Aug 2018 22:46:17 GMT /slideshow/multiobjective-optimization-using-nondominated-sorting-genetic-algorithm-with-numerical-example-stepbystep/111832756 AhmedGadFCIT@slideshare.net(AhmedGadFCIT) Multi-Objective Optimization using Non-Dominated Sorting Genetic Algorithm with Numerical Example Step-by-Step AhmedGadFCIT When solving a problem, the goal is not only solving it but also optimizing such solution. There might be multiple solutions to a problem and the challenge is to find the best of them. The more metrics defining the solution goodness, the harder finding the best solution. This presentation discusses one of the multi-objective optimization techniques called non-dominated sorting genetic algorithm II (NSGA-II) explaining its steps including non-dominated sorting, crowding distance, tournament selection, and genetic algorithm. The presentation works through a numerical example step-by-step. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nsga-180827224617-thumbnail.jpg?width=120&height=120&fit=bounds" /> When solving a problem, the goal is not only solving it but also optimizing such solution. There might be multiple solutions to a problem and the challenge is to find the best of them. The more metrics defining the solution goodness, the harder finding the best solution. This presentation discusses one of the multi-objective optimization techniques called non-dominated sorting genetic algorithm II (NSGA-II) explaining its steps including non-dominated sorting, crowding distance, tournament selection, and genetic algorithm. The presentation works through a numerical example step-by-step.

Multi-Objective Optimization using Non-Dominated Sorting Genetic Algorithm with Numerical Example Step-by-Step from Ahmed Gad

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0 M.Sc. Thesis - Automatic People Counting in Crowded Scenes /slideshow/msc-thesis-automatic-people-counting-in-crowded-scenes-110271518/110271518 m-180817133402
This thesis proposes a real-time automatic people crowd density estimation method for overcoming the non-linearity problem, working with different densities and scales, and enhancing the prediction error. To cover most of the properties of the crowded scene, a newly used combination of features is proposed that includes segmented region properties, texture, edge, and SIFT keypoints. Edge strength is a suggested for use.]]>
This thesis proposes a real-time automatic people crowd density estimation method for overcoming the non-linearity problem, working with different densities and scales, and enhancing the prediction error. To cover most of the properties of the crowded scene, a newly used combination of features is proposed that includes segmented region properties, texture, edge, and SIFT keypoints. Edge strength is a suggested for use.]]> Fri, 17 Aug 2018 13:34:02 GMT /slideshow/msc-thesis-automatic-people-counting-in-crowded-scenes-110271518/110271518 AhmedGadFCIT@slideshare.net(AhmedGadFCIT) M.Sc. Thesis - Automatic People Counting in Crowded Scenes AhmedGadFCIT This thesis proposes a real-time automatic people crowd density estimation method for overcoming the non-linearity problem, working with different densities and scales, and enhancing the prediction error. To cover most of the properties of the crowded scene, a newly used combination of features is proposed that includes segmented region properties, texture, edge, and SIFT keypoints. Edge strength is a suggested for use. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/m-180817133402-thumbnail.jpg?width=120&height=120&fit=bounds" /> This thesis proposes a real-time automatic people crowd density estimation method for overcoming the non-linearity problem, working with different densities and scales, and enhancing the prediction error. To cover most of the properties of the crowded scene, a newly used combination of features is proposed that includes segmented region properties, texture, edge, and SIFT keypoints. Edge strength is a suggested for use.

M.Sc. Thesis - Automatic People Counting in Crowded Scenes from Ahmed Gad

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0 Derivation of Convolutional Neural Network from Fully Connected Network Step-by-Step /slideshow/derivation-of-convolutional-neural-network-from-fully-connected-network-stepbystep/97561557 derivationofconvolutionalneuralnetworkfromfullyconnectednetworkstep-by-step-180518234203
In image analysis, #convolutional neural networks (#CNNs or #ConvNets for short) are time and memory efficient than fully connected (#FC) networks. But why? What are the advantages of ConvNets over FC networks in image analysis? How is #ConvNet derived from FC networks? Where the term #convolution in CNNs came from? These questions are to be answered in this #presentation. Image analysis has a number of challenges such as classification, object detection, recognition, description, etc. If an image classifier, for example, is to be created, it should be able to work with a high accuracy even with variations such as occlusion, illumination changes, viewing angles, and others. The traditional pipeline of image classification with its main step of feature engineering is not suitable for working in rich environments. Even experts in the field won’t be able to give a single or a group of features that are able to reach high accuracy under different variations. Motivated by this problem, the idea of feature learning came out. The suitable features to work with images are learned automatically. This is the reason why artificial neural networks (ANNs) are one of the robust ways of image analysis. Based on a learning algorithm such as gradient descent (GD), ANN learns the image features automatically. The raw image is applied to the ANN and ANN is responsible for generating the features describing it.]]>
In image analysis, #convolutional neural networks (#CNNs or #ConvNets for short) are time and memory efficient than fully connected (#FC) networks. But why? What are the advantages of ConvNets over FC networks in image analysis? How is #ConvNet derived from FC networks? Where the term #convolution in CNNs came from? These questions are to be answered in this #presentation. Image analysis has a number of challenges such as classification, object detection, recognition, description, etc. If an image classifier, for example, is to be created, it should be able to work with a high accuracy even with variations such as occlusion, illumination changes, viewing angles, and others. The traditional pipeline of image classification with its main step of feature engineering is not suitable for working in rich environments. Even experts in the field won’t be able to give a single or a group of features that are able to reach high accuracy under different variations. Motivated by this problem, the idea of feature learning came out. The suitable features to work with images are learned automatically. This is the reason why artificial neural networks (ANNs) are one of the robust ways of image analysis. Based on a learning algorithm such as gradient descent (GD), ANN learns the image features automatically. The raw image is applied to the ANN and ANN is responsible for generating the features describing it.]]> Fri, 18 May 2018 23:42:03 GMT /slideshow/derivation-of-convolutional-neural-network-from-fully-connected-network-stepbystep/97561557 AhmedGadFCIT@slideshare.net(AhmedGadFCIT) Derivation of Convolutional Neural Network from Fully Connected Network Step-by-Step AhmedGadFCIT In image analysis, #convolutional neural networks (#CNNs or #ConvNets for short) are time and memory efficient than fully connected (#FC) networks. But why? What are the advantages of ConvNets over FC networks in image analysis? How is #ConvNet derived from FC networks? Where the term #convolution in CNNs came from? These questions are to be answered in this #presentation. Image analysis has a number of challenges such as classification, object detection, recognition, description, etc. If an image classifier, for example, is to be created, it should be able to work with a high accuracy even with variations such as occlusion, illumination changes, viewing angles, and others. The traditional pipeline of image classification with its main step of feature engineering is not suitable for working in rich environments. Even experts in the field won’t be able to give a single or a group of features that are able to reach high accuracy under different variations. Motivated by this problem, the idea of feature learning came out. The suitable features to work with images are learned automatically. This is the reason why artificial neural networks (ANNs) are one of the robust ways of image analysis. Based on a learning algorithm such as gradient descent (GD), ANN learns the image features automatically. The raw image is applied to the ANN and ANN is responsible for generating the features describing it. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/derivationofconvolutionalneuralnetworkfromfullyconnectednetworkstep-by-step-180518234203-thumbnail.jpg?width=120&height=120&fit=bounds" /> In image analysis, #convolutional neural networks (#CNNs or #ConvNets for short) are time and memory efficient than fully connected (#FC) networks. But why? What are the advantages of ConvNets over FC networks in image analysis? How is #ConvNet derived from FC networks? Where the term #convolution in CNNs came from? These questions are to be answered in this #presentation. Image analysis has a number of challenges such as classification, object detection, recognition, description, etc. If an image classifier, for example, is to be created, it should be able to work with a high accuracy even with variations such as occlusion, illumination changes, viewing angles, and others. The traditional pipeline of image classification with its main step of feature engineering is not suitable for working in rich environments. Even experts in the field won’t be able to give a single or a group of features that are able to reach high accuracy under different variations. Motivated by this problem, the idea of feature learning came out. The suitable features to work with images are learned automatically. This is the reason why artificial neural networks (ANNs) are one of the robust ways of image analysis. Based on a learning algorithm such as gradient descent (GD), ANN learns the image features automatically. The raw image is applied to the ANN and ANN is responsible for generating the features describing it.

Derivation of Convolutional Neural Network from Fully Connected Network Step-by-Step from Ahmed Gad

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0 Introduction to Optimization with Genetic Algorithm (GA) /slideshow/introduction-to-optimization-with-genetic-algorithm-ga/93401588 shared-introductiontooptimizationwithgeneticalgorithm-180410043704
Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This article gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs. References: Eiben, Agoston E., and James E. Smith. Introduction to evolutionary computing. Vol. 53. Heidelberg: springer, 2003. https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html]]>
Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This article gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs. References: Eiben, Agoston E., and James E. Smith. Introduction to evolutionary computing. Vol. 53. Heidelberg: springer, 2003. https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html]]> Tue, 10 Apr 2018 04:37:04 GMT /slideshow/introduction-to-optimization-with-genetic-algorithm-ga/93401588 AhmedGadFCIT@slideshare.net(AhmedGadFCIT) Introduction to Optimization with Genetic Algorithm (GA) AhmedGadFCIT Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This article gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs. References: Eiben, Agoston E., and James E. Smith. Introduction to evolutionary computing. Vol. 53. Heidelberg: springer, 2003. https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/shared-introductiontooptimizationwithgeneticalgorithm-180410043704-thumbnail.jpg?width=120&height=120&fit=bounds" /> Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This article gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs. References: Eiben, Agoston E., and James E. Smith. Introduction to evolutionary computing. Vol. 53. Heidelberg: springer, 2003. https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html

Introduction to Optimization with Genetic Algorithm (GA) from Ahmed Gad

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0 Derivation of Convolutional Neural Network (ConvNet) from Fully Connected Network /slideshow/derivation-of-convolutional-neural-network-convnet-from-fully-connected-network/93399767 shared-derivationofconvnetfromfullyconnectednetwork-180410041334
In image analysis, convolutional neural networks (CNNs or ConvNets for short) are time and memory efficient than fully connected (FC) networks. But why? What are the advantages of ConvNets over FC networks in image analysis? How is ConvNet derived from FC networks? Where the term convolution in CNNs came from? These questions are to be answered in this article. Image analysis has a number of challenges such as classification, object detection, recognition, description, etc. If an image classifier, for example, is to be created, it should be able to work with a high accuracy even with variations such as occlusion, illumination changes, viewing angles, and others. The traditional pipeline of image classification with its main step of feature engineering is not suitable for working in rich environments. Even experts in the field won’t be able to give a single or a group of features that are able to reach high accuracy under different variations. Motivated by this problem, the idea of feature learning came out. The suitable features to work with images are learned automatically. This is the reason why artificial neural networks (ANNs) are one of the robust ways of image analysis. Based on a learning algorithm such as gradient descent (GD), ANN learns the image features automatically. The raw image is applied to the ANN and ANN is responsible for generating the features describing it. -Reference Aghdam, Hamed Habibi, and Elnaz Jahani Heravi. Guide to Convolutional Neural Networks: A Practical Application to Traffic-Sign Detection and Classification. Springer, 2017.]]>
In image analysis, convolutional neural networks (CNNs or ConvNets for short) are time and memory efficient than fully connected (FC) networks. But why? What are the advantages of ConvNets over FC networks in image analysis? How is ConvNet derived from FC networks? Where the term convolution in CNNs came from? These questions are to be answered in this article. Image analysis has a number of challenges such as classification, object detection, recognition, description, etc. If an image classifier, for example, is to be created, it should be able to work with a high accuracy even with variations such as occlusion, illumination changes, viewing angles, and others. The traditional pipeline of image classification with its main step of feature engineering is not suitable for working in rich environments. Even experts in the field won’t be able to give a single or a group of features that are able to reach high accuracy under different variations. Motivated by this problem, the idea of feature learning came out. The suitable features to work with images are learned automatically. This is the reason why artificial neural networks (ANNs) are one of the robust ways of image analysis. Based on a learning algorithm such as gradient descent (GD), ANN learns the image features automatically. The raw image is applied to the ANN and ANN is responsible for generating the features describing it. -Reference Aghdam, Hamed Habibi, and Elnaz Jahani Heravi. Guide to Convolutional Neural Networks: A Practical Application to Traffic-Sign Detection and Classification. Springer, 2017.]]> Tue, 10 Apr 2018 04:13:34 GMT /slideshow/derivation-of-convolutional-neural-network-convnet-from-fully-connected-network/93399767 AhmedGadFCIT@slideshare.net(AhmedGadFCIT) Derivation of Convolutional Neural Network (ConvNet) from Fully Connected Network AhmedGadFCIT In image analysis, convolutional neural networks (CNNs or ConvNets for short) are time and memory efficient than fully connected (FC) networks. But why? What are the advantages of ConvNets over FC networks in image analysis? How is ConvNet derived from FC networks? Where the term convolution in CNNs came from? These questions are to be answered in this article. Image analysis has a number of challenges such as classification, object detection, recognition, description, etc. If an image classifier, for example, is to be created, it should be able to work with a high accuracy even with variations such as occlusion, illumination changes, viewing angles, and others. The traditional pipeline of image classification with its main step of feature engineering is not suitable for working in rich environments. Even experts in the field won’t be able to give a single or a group of features that are able to reach high accuracy under different variations. Motivated by this problem, the idea of feature learning came out. The suitable features to work with images are learned automatically. This is the reason why artificial neural networks (ANNs) are one of the robust ways of image analysis. Based on a learning algorithm such as gradient descent (GD), ANN learns the image features automatically. The raw image is applied to the ANN and ANN is responsible for generating the features describing it. -Reference Aghdam, Hamed Habibi, and Elnaz Jahani Heravi. Guide to Convolutional Neural Networks: A Practical Application to Traffic-Sign Detection and Classification. Springer, 2017. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/shared-derivationofconvnetfromfullyconnectednetwork-180410041334-thumbnail.jpg?width=120&height=120&fit=bounds" /> In image analysis, convolutional neural networks (CNNs or ConvNets for short) are time and memory efficient than fully connected (FC) networks. But why? What are the advantages of ConvNets over FC networks in image analysis? How is ConvNet derived from FC networks? Where the term convolution in CNNs came from? These questions are to be answered in this article. Image analysis has a number of challenges such as classification, object detection, recognition, description, etc. If an image classifier, for example, is to be created, it should be able to work with a high accuracy even with variations such as occlusion, illumination changes, viewing angles, and others. The traditional pipeline of image classification with its main step of feature engineering is not suitable for working in rich environments. Even experts in the field won’t be able to give a single or a group of features that are able to reach high accuracy under different variations. Motivated by this problem, the idea of feature learning came out. The suitable features to work with images are learned automatically. This is the reason why artificial neural networks (ANNs) are one of the robust ways of image analysis. Based on a learning algorithm such as gradient descent (GD), ANN learns the image features automatically. The raw image is applied to the ANN and ANN is responsible for generating the features describing it. -Reference Aghdam, Hamed Habibi, and Elnaz Jahani Heravi. Guide to Convolutional Neural Networks: A Practical Application to Traffic-Sign Detection and Classification. Springer, 2017.

Derivation of Convolutional Neural Network (ConvNet) from Fully Connected Network from Ahmed Gad

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0 Avoid Overfitting with Regularization /slideshow/avoid-overfitting-with-regularization/93399376 shared-avoidoverfittingwithregularization-180410040809
Have you ever created a machine learning model that is perfect for the training samples but gives very bad predictions with unseen samples! Did you ever think why this happens? This article explains overfitting which is one of the reasons for poor predictions for unseen samples. Also, regularization technique based on regression is presented by simple steps to make it clear how to avoid overfitting.]]>
Have you ever created a machine learning model that is perfect for the training samples but gives very bad predictions with unseen samples! Did you ever think why this happens? This article explains overfitting which is one of the reasons for poor predictions for unseen samples. Also, regularization technique based on regression is presented by simple steps to make it clear how to avoid overfitting.]]> Tue, 10 Apr 2018 04:08:09 GMT /slideshow/avoid-overfitting-with-regularization/93399376 AhmedGadFCIT@slideshare.net(AhmedGadFCIT) Avoid Overfitting with Regularization AhmedGadFCIT Have you ever created a machine learning model that is perfect for the training samples but gives very bad predictions with unseen samples! Did you ever think why this happens? This article explains overfitting which is one of the reasons for poor predictions for unseen samples. Also, regularization technique based on regression is presented by simple steps to make it clear how to avoid overfitting. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/shared-avoidoverfittingwithregularization-180410040809-thumbnail.jpg?width=120&height=120&fit=bounds" /> Have you ever created a machine learning model that is perfect for the training samples but gives very bad predictions with unseen samples! Did you ever think why this happens? This article explains overfitting which is one of the reasons for poor predictions for unseen samples. Also, regularization technique based on regression is presented by simple steps to make it clear how to avoid overfitting.

Avoid Overfitting with Regularization from Ahmed Gad

]]> 689 0 https://cdn.slidesharecdn.com/ss_thumbnails/shared-avoidoverfittingwithregularization-180410040809-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post

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0 Genetic Algorithm (GA) Optimization - Step-by-Step Example /slideshow/genetic-algorithm-ga-optimization-stepbystep-example/93398028 geneticalgorithmgaoptimization-step-by-stepexample-180410034952
Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This presentation gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs. A step-by-step example is given in addition to its implementation in Python 3.5. --------------------------------- Read more about GA: Yu, Xinjie, and Mitsuo Gen. Introduction to evolutionary algorithms. Springer Science & Business Media, 2010. https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad]]>
Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This presentation gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs. A step-by-step example is given in addition to its implementation in Python 3.5. --------------------------------- Read more about GA: Yu, Xinjie, and Mitsuo Gen. Introduction to evolutionary algorithms. Springer Science & Business Media, 2010. https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad]]> Tue, 10 Apr 2018 03:49:52 GMT /slideshow/genetic-algorithm-ga-optimization-stepbystep-example/93398028 AhmedGadFCIT@slideshare.net(AhmedGadFCIT) Genetic Algorithm (GA) Optimization - Step-by-Step Example AhmedGadFCIT Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This presentation gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs. A step-by-step example is given in addition to its implementation in Python 3.5. --------------------------------- Read more about GA: Yu, Xinjie, and Mitsuo Gen. Introduction to evolutionary algorithms. Springer Science & Business Media, 2010. https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/geneticalgorithmgaoptimization-step-by-stepexample-180410034952-thumbnail.jpg?width=120&height=120&fit=bounds" /> Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This presentation gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs. A step-by-step example is given in addition to its implementation in Python 3.5. --------------------------------- Read more about GA: Yu, Xinjie, and Mitsuo Gen. Introduction to evolutionary algorithms. Springer Science & Business Media, 2010. https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad

Genetic Algorithm (GA) Optimization - Step-by-Step Example from Ahmed Gad

]]> 36706 7 https://cdn.slidesharecdn.com/ss_thumbnails/geneticalgorithmgaoptimization-step-by-stepexample-180410034952-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 ICCES 2017 - Crowd Density Estimation Method using Regression Analysis /slideshow/icces-2017-crowd-density-estimation-method-using-regression-analysis/84558468 pid107-20-12-2017-icces2017-171220180350
The oral presentation of the paper titled "Crowd Density Estimation Method using Multiple Feature Categories and Multiple Regression Models". This paper was accepted for publication and oral presentation in the 12th IEEE International Conference on Computer Engineering and Systems (ICCES 2017) held from 19 to 20 December 2017 in Cairo, Egypt. The paper proposed a new method to estimate the number of people within crowded scenes using regression analysis. The two challenges in crowd density estimation using regression analysis are perspective distortion and non-linearity. This paper solves the perspective distortion using perspective normalization which is the best way to deal with that problem based on recent works. The second challenge is solved by creating a new combination of features collected from multiple already existing categories including segmented region, texture, edge, and keypoints. This paper created a feature vector of length 164. Five regression models are used which are GPR, RF, RPF, LASSO, and KNN. Based on the experimental results, our proposed method gives better results than previous works. ---------------------------------- أحمد فوزي جاد Ahmed Fawzy Gad قسم تكنولوجيا المعلومات Information Technology (IT) Department كلية الحاسبات والمعلومات Faculty of Computers and Information (FCI) جامعة المنوفية, مصر Menoufia University, Egypt Teaching Assistant/Demonstrator ahmed.fawzy@ci.menofia.edu.eg --------------------------------- Find me on: Blog (Arabic) https://aiage-ar.blogspot.com.eg/ (English) https://aiage.blogspot.com.eg/ YouTube https://www.youtube.com/AhmedGadFCIT Google Plus https://plus.google.com/u/0/+AhmedGadIT �ݺ�ߣShare /AhmedGadFCIT LinkedIn https://www.linkedin.com/in/ahmedfgad reddit https://www.reddit.com/user/AhmedGadFCIT ResearchGate https://www.researchgate.net/profile/Ahmed_Gad13 Academia https://menofia.academia.edu/Gad Google Scholar https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en Mendelay https://www.mendeley.com/profiles/ahmed-gad12 ORCID https://orcid.org/0000-0003-1978-8574 StackOverFlow http://stackoverflow.com/users/5426539/ahmed-gad Twitter https://twitter.com/ahmedfgad Facebook https://www.facebook.com/ahmed.f.gadd Pinterest https://www.pinterest.com/ahmedfgad]]>
The oral presentation of the paper titled "Crowd Density Estimation Method using Multiple Feature Categories and Multiple Regression Models". This paper was accepted for publication and oral presentation in the 12th IEEE International Conference on Computer Engineering and Systems (ICCES 2017) held from 19 to 20 December 2017 in Cairo, Egypt. The paper proposed a new method to estimate the number of people within crowded scenes using regression analysis. The two challenges in crowd density estimation using regression analysis are perspective distortion and non-linearity. This paper solves the perspective distortion using perspective normalization which is the best way to deal with that problem based on recent works. The second challenge is solved by creating a new combination of features collected from multiple already existing categories including segmented region, texture, edge, and keypoints. This paper created a feature vector of length 164. Five regression models are used which are GPR, RF, RPF, LASSO, and KNN. Based on the experimental results, our proposed method gives better results than previous works. ---------------------------------- أحمد فوزي جاد Ahmed Fawzy Gad قسم تكنولوجيا المعلومات Information Technology (IT) Department كلية الحاسبات والمعلومات Faculty of Computers and Information (FCI) جامعة المنوفية, مصر Menoufia University, Egypt Teaching Assistant/Demonstrator ahmed.fawzy@ci.menofia.edu.eg --------------------------------- Find me on: Blog (Arabic) https://aiage-ar.blogspot.com.eg/ (English) https://aiage.blogspot.com.eg/ YouTube https://www.youtube.com/AhmedGadFCIT Google Plus https://plus.google.com/u/0/+AhmedGadIT �ݺ�ߣShare /AhmedGadFCIT LinkedIn https://www.linkedin.com/in/ahmedfgad reddit https://www.reddit.com/user/AhmedGadFCIT ResearchGate https://www.researchgate.net/profile/Ahmed_Gad13 Academia https://menofia.academia.edu/Gad Google Scholar https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en Mendelay https://www.mendeley.com/profiles/ahmed-gad12 ORCID https://orcid.org/0000-0003-1978-8574 StackOverFlow http://stackoverflow.com/users/5426539/ahmed-gad Twitter https://twitter.com/ahmedfgad Facebook https://www.facebook.com/ahmed.f.gadd Pinterest https://www.pinterest.com/ahmedfgad]]> Wed, 20 Dec 2017 18:03:50 GMT /slideshow/icces-2017-crowd-density-estimation-method-using-regression-analysis/84558468 AhmedGadFCIT@slideshare.net(AhmedGadFCIT) ICCES 2017 - Crowd Density Estimation Method using Regression Analysis AhmedGadFCIT The oral presentation of the paper titled "Crowd Density Estimation Method using Multiple Feature Categories and Multiple Regression Models". This paper was accepted for publication and oral presentation in the 12th IEEE International Conference on Computer Engineering and Systems (ICCES 2017) held from 19 to 20 December 2017 in Cairo, Egypt. The paper proposed a new method to estimate the number of people within crowded scenes using regression analysis. The two challenges in crowd density estimation using regression analysis are perspective distortion and non-linearity. This paper solves the perspective distortion using perspective normalization which is the best way to deal with that problem based on recent works. The second challenge is solved by creating a new combination of features collected from multiple already existing categories including segmented region, texture, edge, and keypoints. This paper created a feature vector of length 164. Five regression models are used which are GPR, RF, RPF, LASSO, and KNN. Based on the experimental results, our proposed method gives better results than previous works. ---------------------------------- أحمد فوزي جاد Ahmed Fawzy Gad قسم تكنولوجيا المعلومات Information Technology (IT) Department كلية الحاسبات والمعلومات Faculty of Computers and Information (FCI) جامعة المنوفية, مصر Menoufia University, Egypt Teaching Assistant/Demonstrator ahmed.fawzy@ci.menofia.edu.eg --------------------------------- Find me on: Blog (Arabic) https://aiage-ar.blogspot.com.eg/ (English) https://aiage.blogspot.com.eg/ YouTube https://www.youtube.com/AhmedGadFCIT Google Plus https://plus.google.com/u/0/+AhmedGadIT �ݺ�ߣShare /AhmedGadFCIT LinkedIn https://www.linkedin.com/in/ahmedfgad reddit https://www.reddit.com/user/AhmedGadFCIT ResearchGate https://www.researchgate.net/profile/Ahmed_Gad13 Academia https://menofia.academia.edu/Gad Google Scholar https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en Mendelay https://www.mendeley.com/profiles/ahmed-gad12 ORCID https://orcid.org/0000-0003-1978-8574 StackOverFlow http://stackoverflow.com/users/5426539/ahmed-gad Twitter https://twitter.com/ahmedfgad Facebook https://www.facebook.com/ahmed.f.gadd Pinterest https://www.pinterest.com/ahmedfgad <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pid107-20-12-2017-icces2017-171220180350-thumbnail.jpg?width=120&height=120&fit=bounds" /> The oral presentation of the paper titled "Crowd Density Estimation Method using Multiple Feature Categories and Multiple Regression Models". This paper was accepted for publication and oral presentation in the 12th IEEE International Conference on Computer Engineering and Systems (ICCES 2017) held from 19 to 20 December 2017 in Cairo, Egypt. The paper proposed a new method to estimate the number of people within crowded scenes using regression analysis. The two challenges in crowd density estimation using regression analysis are perspective distortion and non-linearity. This paper solves the perspective distortion using perspective normalization which is the best way to deal with that problem based on recent works. The second challenge is solved by creating a new combination of features collected from multiple already existing categories including segmented region, texture, edge, and keypoints. This paper created a feature vector of length 164. Five regression models are used which are GPR, RF, RPF, LASSO, and KNN. Based on the experimental results, our proposed method gives better results than previous works. ---------------------------------- أحمد فوزي جاد Ahmed Fawzy Gad قسم تكنولوجيا المعلومات Information Technology (IT) Department كلية الحاسبات والمعلومات Faculty of Computers and Information (FCI) جامعة المنوفية, مصر Menoufia University, Egypt Teaching Assistant/Demonstrator ahmed.fawzy@ci.menofia.edu.eg --------------------------------- Find me on: Blog (Arabic) https://aiage-ar.blogspot.com.eg/ (English) https://aiage.blogspot.com.eg/ YouTube https://www.youtube.com/AhmedGadFCIT Google Plus https://plus.google.com/u/0/+AhmedGadIT �ݺ�ߣShare /AhmedGadFCIT LinkedIn https://www.linkedin.com/in/ahmedfgad reddit https://www.reddit.com/user/AhmedGadFCIT ResearchGate https://www.researchgate.net/profile/Ahmed_Gad13 Academia https://menofia.academia.edu/Gad Google Scholar https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en Mendelay https://www.mendeley.com/profiles/ahmed-gad12 ORCID https://orcid.org/0000-0003-1978-8574 StackOverFlow http://stackoverflow.com/users/5426539/ahmed-gad Twitter https://twitter.com/ahmedfgad Facebook https://www.facebook.com/ahmed.f.gadd Pinterest https://www.pinterest.com/ahmedfgad

ICCES 2017 - Crowd Density Estimation Method using Regression Analysis from Ahmed Gad

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0 Backpropagation: Understanding How to Update ANNs Weights Step-by-Step /slideshow/backpropagation-understanding-how-to-update-anns-weights-stepbystep/81997337 backpropagationunderstandinghowtoupdateannsweightsstep-by-step-171113173200
This presentation explains how the backpropagation algorithm is useful in updating the artificial neural networks (ANNs) weights using two examples step by step. Readers should have a basic understanding of how ANNs work, partial derivatives, and multivariate chain rule. This presentation won`t dive directly into the details of the algorithm but will start by training a very simple network. This is because the backpropagation algorithm is meant to be applied over a network after training. So, we should train the network before applying it to catch the benefits of backpropagation algorithm and how to use it. ]]>
This presentation explains how the backpropagation algorithm is useful in updating the artificial neural networks (ANNs) weights using two examples step by step. Readers should have a basic understanding of how ANNs work, partial derivatives, and multivariate chain rule. This presentation won`t dive directly into the details of the algorithm but will start by training a very simple network. This is because the backpropagation algorithm is meant to be applied over a network after training. So, we should train the network before applying it to catch the benefits of backpropagation algorithm and how to use it. ]]> Mon, 13 Nov 2017 17:32:00 GMT /slideshow/backpropagation-understanding-how-to-update-anns-weights-stepbystep/81997337 AhmedGadFCIT@slideshare.net(AhmedGadFCIT) Backpropagation: Understanding How to Update ANNs Weights Step-by-Step AhmedGadFCIT This presentation explains how the backpropagation algorithm is useful in updating the artificial neural networks (ANNs) weights using two examples step by step. Readers should have a basic understanding of how ANNs work, partial derivatives, and multivariate chain rule. This presentation won`t dive directly into the details of the algorithm but will start by training a very simple network. This is because the backpropagation algorithm is meant to be applied over a network after training. So, we should train the network before applying it to catch the benefits of backpropagation algorithm and how to use it. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/backpropagationunderstandinghowtoupdateannsweightsstep-by-step-171113173200-thumbnail.jpg?width=120&height=120&fit=bounds" /> This presentation explains how the backpropagation algorithm is useful in updating the artificial neural networks (ANNs) weights using two examples step by step. Readers should have a basic understanding of how ANNs work, partial derivatives, and multivariate chain rule. This presentation won`t dive directly into the details of the algorithm but will start by training a very simple network. This is because the backpropagation algorithm is meant to be applied over a network after training. So, we should train the network before applying it to catch the benefits of backpropagation algorithm and how to use it.

Backpropagation: Understanding How to Update ANNs Weights Step-by-Step from Ahmed Gad

]]> 6692 10 https://cdn.slidesharecdn.com/ss_thumbnails/backpropagationunderstandinghowtoupdateannsweightsstep-by-step-171113173200-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 Computer Vision: Correlation, Convolution, and Gradient /slideshow/computer-vision-correlation-convolution-and-gradient/80308604 2-170930051328
Three important operations in computer vision are explained starting with each one got explained and implemented in Python. Generally, all of these three operations have many similarities in as they follow the same general steps but there are some subtle changes. The main change is using different masks.]]>
Three important operations in computer vision are explained starting with each one got explained and implemented in Python. Generally, all of these three operations have many similarities in as they follow the same general steps but there are some subtle changes. The main change is using different masks.]]> Sat, 30 Sep 2017 05:13:28 GMT /slideshow/computer-vision-correlation-convolution-and-gradient/80308604 AhmedGadFCIT@slideshare.net(AhmedGadFCIT) Computer Vision: Correlation, Convolution, and Gradient AhmedGadFCIT Three important operations in computer vision are explained starting with each one got explained and implemented in Python. Generally, all of these three operations have many similarities in as they follow the same general steps but there are some subtle changes. The main change is using different masks. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2-170930051328-thumbnail.jpg?width=120&height=120&fit=bounds" /> Three important operations in computer vision are explained starting with each one got explained and implemented in Python. Generally, all of these three operations have many similarities in as they follow the same general steps but there are some subtle changes. The main change is using different masks.

Computer Vision: Correlation, Convolution, and Gradient from Ahmed Gad

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0 Python for Computer Vision - Revision /slideshow/python-for-computer-vision/80110602 pythonforcomputervision-170924221538
A brief review about Python for computer vision showing the different modules necessary to dive into computer vision. The modules presented are NumPy, SciPy, and Matplotlib. ]]>
A brief review about Python for computer vision showing the different modules necessary to dive into computer vision. The modules presented are NumPy, SciPy, and Matplotlib. ]]> Sun, 24 Sep 2017 22:15:38 GMT /slideshow/python-for-computer-vision/80110602 AhmedGadFCIT@slideshare.net(AhmedGadFCIT) Python for Computer Vision - Revision AhmedGadFCIT A brief review about Python for computer vision showing the different modules necessary to dive into computer vision. The modules presented are NumPy, SciPy, and Matplotlib. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pythonforcomputervision-170924221538-thumbnail.jpg?width=120&height=120&fit=bounds" /> A brief review about Python for computer vision showing the different modules necessary to dive into computer vision. The modules presented are NumPy, SciPy, and Matplotlib.

Python for Computer Vision - Revision from Ahmed Gad

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0 Anime Studio Pro 10 Tutorial as Part of Multimedia Course /slideshow/anime-studio-tutorial-as-part-of-multimedia-course/80024281 documentation-170921161222
There are different ways of presenting information to users. These ways are called medias because they similar to networking media that carry data from one place to another, they carry information from the source to the user. Examples of medias are text, image, sound, video, animation. Because multiple types of medias can be used to carry the same piece of information, there is what is called multimedia (MM). This is a combined set of medias working together to present the information in a friendly way to the end-user. The use of one media depends on the type of audience and the type of information to be presented. One media may be powerful over another to present some types of information. The primary goals of this course is to make you understand the different types of medias, use cases of one media over another, and combining different media types. Also this course tells how to create such types of medias to create interactive media.]]>
There are different ways of presenting information to users. These ways are called medias because they similar to networking media that carry data from one place to another, they carry information from the source to the user. Examples of medias are text, image, sound, video, animation. Because multiple types of medias can be used to carry the same piece of information, there is what is called multimedia (MM). This is a combined set of medias working together to present the information in a friendly way to the end-user. The use of one media depends on the type of audience and the type of information to be presented. One media may be powerful over another to present some types of information. The primary goals of this course is to make you understand the different types of medias, use cases of one media over another, and combining different media types. Also this course tells how to create such types of medias to create interactive media.]]> Thu, 21 Sep 2017 16:12:22 GMT /slideshow/anime-studio-tutorial-as-part-of-multimedia-course/80024281 AhmedGadFCIT@slideshare.net(AhmedGadFCIT) Anime Studio Pro 10 Tutorial as Part of Multimedia Course AhmedGadFCIT There are different ways of presenting information to users. These ways are called medias because they similar to networking media that carry data from one place to another, they carry information from the source to the user. Examples of medias are text, image, sound, video, animation. Because multiple types of medias can be used to carry the same piece of information, there is what is called multimedia (MM). This is a combined set of medias working together to present the information in a friendly way to the end-user. The use of one media depends on the type of audience and the type of information to be presented. One media may be powerful over another to present some types of information. The primary goals of this course is to make you understand the different types of medias, use cases of one media over another, and combining different media types. Also this course tells how to create such types of medias to create interactive media. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/documentation-170921161222-thumbnail.jpg?width=120&height=120&fit=bounds" /> There are different ways of presenting information to users. These ways are called medias because they similar to networking media that carry data from one place to another, they carry information from the source to the user. Examples of medias are text, image, sound, video, animation. Because multiple types of medias can be used to carry the same piece of information, there is what is called multimedia (MM). This is a combined set of medias working together to present the information in a friendly way to the end-user. The use of one media depends on the type of audience and the type of information to be presented. One media may be powerful over another to present some types of information. The primary goals of this course is to make you understand the different types of medias, use cases of one media over another, and combining different media types. Also this course tells how to create such types of medias to create interactive media.

Anime Studio Pro 10 Tutorial as Part of Multimedia Course from Ahmed Gad

]]> 1891 6 https://cdn.slidesharecdn.com/ss_thumbnails/documentation-170921161222-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post

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0 Brief Introduction to Deep Learning + Solving XOR using ANNs /slideshow/brief-introduction-to-deep-learning-solving-xor-using-anns/78174697 briefintroductiontodeeplearningsolvingxorusingann-shared-170723215849
This presentation gives a very simple introduction to deep learning in addition to a step-by-step example showing how to solve the XOR non-linear problem using multi-layer artificial neural networks that has both input, hidden, and output layers. Deep learning is based on artificial neural networks and it aims to analyze large amounts of data that are not easily analyzed using conventional models. It creates a large neural network with several hidden layers and several neurons within each layer and usually may take days for its learning. Many beginners in artificial neural networks have a problem in understanding how hidden layers are useful and what is the best number of hidden layers and best number of neurons or nodes within each layer. أحمد فوزي جاد Ahmed Fawzy Gad قسم تكنولوجيا المعلومات Information Technology (IT) Department كلية الحاسبات والمعلومات Faculty of Computers and Information (FCI) جامعة المنوفية, مصر Menoufia University, Egypt Teaching Assistant/Demonstrator ahmed.fawzy@ci.menofia.edu.eg : AFCIT http://www.afcit.xyz YouTube https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw Google Plus https://plus.google.com/u/0/+AhmedGadIT �ݺ�ߣShare /AhmedGadFCIT LinkedIn https://www.linkedin.com/in/ahmedfgad/ ResearchGate https://www.researchgate.net/profile/Ahmed_Gad13 Academia https://menofia.academia.edu/Gad Google Scholar https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en Mendelay https://www.mendeley.com/profiles/ahmed-gad12/ ORCID https://orcid.org/0000-0003-1978-8574 StackOverFlow http://stackoverflow.com/users/5426539/ahmed-gad Twitter https://twitter.com/ahmedfgad Facebook https://www.facebook.com/ahmed.f.gadd Pinterest https://www.pinterest.com/ahmedfgad/]]>
This presentation gives a very simple introduction to deep learning in addition to a step-by-step example showing how to solve the XOR non-linear problem using multi-layer artificial neural networks that has both input, hidden, and output layers. Deep learning is based on artificial neural networks and it aims to analyze large amounts of data that are not easily analyzed using conventional models. It creates a large neural network with several hidden layers and several neurons within each layer and usually may take days for its learning. Many beginners in artificial neural networks have a problem in understanding how hidden layers are useful and what is the best number of hidden layers and best number of neurons or nodes within each layer. أحمد فوزي جاد Ahmed Fawzy Gad قسم تكنولوجيا المعلومات Information Technology (IT) Department كلية الحاسبات والمعلومات Faculty of Computers and Information (FCI) جامعة المنوفية, مصر Menoufia University, Egypt Teaching Assistant/Demonstrator ahmed.fawzy@ci.menofia.edu.eg : AFCIT http://www.afcit.xyz YouTube https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw Google Plus https://plus.google.com/u/0/+AhmedGadIT �ݺ�ߣShare /AhmedGadFCIT LinkedIn https://www.linkedin.com/in/ahmedfgad/ ResearchGate https://www.researchgate.net/profile/Ahmed_Gad13 Academia https://menofia.academia.edu/Gad Google Scholar https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en Mendelay https://www.mendeley.com/profiles/ahmed-gad12/ ORCID https://orcid.org/0000-0003-1978-8574 StackOverFlow http://stackoverflow.com/users/5426539/ahmed-gad Twitter https://twitter.com/ahmedfgad Facebook https://www.facebook.com/ahmed.f.gadd Pinterest https://www.pinterest.com/ahmedfgad/]]> Sun, 23 Jul 2017 21:58:49 GMT /slideshow/brief-introduction-to-deep-learning-solving-xor-using-anns/78174697 AhmedGadFCIT@slideshare.net(AhmedGadFCIT) Brief Introduction to Deep Learning + Solving XOR using ANNs AhmedGadFCIT This presentation gives a very simple introduction to deep learning in addition to a step-by-step example showing how to solve the XOR non-linear problem using multi-layer artificial neural networks that has both input, hidden, and output layers. Deep learning is based on artificial neural networks and it aims to analyze large amounts of data that are not easily analyzed using conventional models. It creates a large neural network with several hidden layers and several neurons within each layer and usually may take days for its learning. Many beginners in artificial neural networks have a problem in understanding how hidden layers are useful and what is the best number of hidden layers and best number of neurons or nodes within each layer. أحمد فوزي جاد Ahmed Fawzy Gad قسم تكنولوجيا المعلومات Information Technology (IT) Department كلية الحاسبات والمعلومات Faculty of Computers and Information (FCI) جامعة المنوفية, مصر Menoufia University, Egypt Teaching Assistant/Demonstrator ahmed.fawzy@ci.menofia.edu.eg : AFCIT http://www.afcit.xyz YouTube https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw Google Plus https://plus.google.com/u/0/+AhmedGadIT �ݺ�ߣShare /AhmedGadFCIT LinkedIn https://www.linkedin.com/in/ahmedfgad/ ResearchGate https://www.researchgate.net/profile/Ahmed_Gad13 Academia https://menofia.academia.edu/Gad Google Scholar https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en Mendelay https://www.mendeley.com/profiles/ahmed-gad12/ ORCID https://orcid.org/0000-0003-1978-8574 StackOverFlow http://stackoverflow.com/users/5426539/ahmed-gad Twitter https://twitter.com/ahmedfgad Facebook https://www.facebook.com/ahmed.f.gadd Pinterest https://www.pinterest.com/ahmedfgad/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/briefintroductiontodeeplearningsolvingxorusingann-shared-170723215849-thumbnail.jpg?width=120&height=120&fit=bounds" /> This presentation gives a very simple introduction to deep learning in addition to a step-by-step example showing how to solve the XOR non-linear problem using multi-layer artificial neural networks that has both input, hidden, and output layers. Deep learning is based on artificial neural networks and it aims to analyze large amounts of data that are not easily analyzed using conventional models. It creates a large neural network with several hidden layers and several neurons within each layer and usually may take days for its learning. Many beginners in artificial neural networks have a problem in understanding how hidden layers are useful and what is the best number of hidden layers and best number of neurons or nodes within each layer. أحمد فوزي جاد Ahmed Fawzy Gad قسم تكنولوجيا المعلومات Information Technology (IT) Department كلية الحاسبات والمعلومات Faculty of Computers and Information (FCI) جامعة المنوفية, مصر Menoufia University, Egypt Teaching Assistant/Demonstrator ahmed.fawzy@ci.menofia.edu.eg : AFCIT http://www.afcit.xyz YouTube https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw Google Plus https://plus.google.com/u/0/+AhmedGadIT �ݺ�ߣShare /AhmedGadFCIT LinkedIn https://www.linkedin.com/in/ahmedfgad/ ResearchGate https://www.researchgate.net/profile/Ahmed_Gad13 Academia https://menofia.academia.edu/Gad Google Scholar https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en Mendelay https://www.mendeley.com/profiles/ahmed-gad12/ ORCID https://orcid.org/0000-0003-1978-8574 StackOverFlow http://stackoverflow.com/users/5426539/ahmed-gad Twitter https://twitter.com/ahmedfgad Facebook https://www.facebook.com/ahmed.f.gadd Pinterest https://www.pinterest.com/ahmedfgad/

Brief Introduction to Deep Learning + Solving XOR using ANNs from Ahmed Gad

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0 Operations in Digital Image Processing + Convolution by Example /slideshow/operations-in-digital-image-processing-convolution-by-example/77212271 operationsindigitalimageprocessingconvolutionbyexample-170623170127
Digital image processing operations can be either point or group. This presentation explains both operations (point and group) and shows how convolution works by a numerical example. Ahmed Fawzy Gad ahmed.fawzy@ci.menofia.edu.eg Information Technology Department Faculty of Computers and Information (FCI) Menoufia University Egypt Find me on: AFCIT http://www.afcit.xyz YouTube https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw Google Plus https://plus.google.com/u/0/+AhmedGadIT �ݺ�ߣShare /AhmedGadFCIT LinkedIn https://www.linkedin.com/in/ahmedfgad/ ResearchGate https://www.researchgate.net/profile/Ahmed_Gad13 Academia https://www.academia.edu/ Google Scholar https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en Mendelay https://www.mendeley.com/profiles/ahmed-gad12/ ORCID https://orcid.org/0000-0003-1978-8574 StackOverFlow http://stackoverflow.com/users/5426539/ahmed-gad Twitter https://twitter.com/ahmedfgad Facebook https://www.facebook.com/ahmed.f.gadd Pinterest https://www.pinterest.com/ahmedfgad/]]>
Digital image processing operations can be either point or group. This presentation explains both operations (point and group) and shows how convolution works by a numerical example. Ahmed Fawzy Gad ahmed.fawzy@ci.menofia.edu.eg Information Technology Department Faculty of Computers and Information (FCI) Menoufia University Egypt Find me on: AFCIT http://www.afcit.xyz YouTube https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw Google Plus https://plus.google.com/u/0/+AhmedGadIT �ݺ�ߣShare /AhmedGadFCIT LinkedIn https://www.linkedin.com/in/ahmedfgad/ ResearchGate https://www.researchgate.net/profile/Ahmed_Gad13 Academia https://www.academia.edu/ Google Scholar https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en Mendelay https://www.mendeley.com/profiles/ahmed-gad12/ ORCID https://orcid.org/0000-0003-1978-8574 StackOverFlow http://stackoverflow.com/users/5426539/ahmed-gad Twitter https://twitter.com/ahmedfgad Facebook https://www.facebook.com/ahmed.f.gadd Pinterest https://www.pinterest.com/ahmedfgad/]]> Fri, 23 Jun 2017 17:01:27 GMT /slideshow/operations-in-digital-image-processing-convolution-by-example/77212271 AhmedGadFCIT@slideshare.net(AhmedGadFCIT) Operations in Digital Image Processing + Convolution by Example AhmedGadFCIT Digital image processing operations can be either point or group. This presentation explains both operations (point and group) and shows how convolution works by a numerical example. Ahmed Fawzy Gad ahmed.fawzy@ci.menofia.edu.eg Information Technology Department Faculty of Computers and Information (FCI) Menoufia University Egypt Find me on: AFCIT http://www.afcit.xyz YouTube https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw Google Plus https://plus.google.com/u/0/+AhmedGadIT �ݺ�ߣShare /AhmedGadFCIT LinkedIn https://www.linkedin.com/in/ahmedfgad/ ResearchGate https://www.researchgate.net/profile/Ahmed_Gad13 Academia https://www.academia.edu/ Google Scholar https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en Mendelay https://www.mendeley.com/profiles/ahmed-gad12/ ORCID https://orcid.org/0000-0003-1978-8574 StackOverFlow http://stackoverflow.com/users/5426539/ahmed-gad Twitter https://twitter.com/ahmedfgad Facebook https://www.facebook.com/ahmed.f.gadd Pinterest https://www.pinterest.com/ahmedfgad/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/operationsindigitalimageprocessingconvolutionbyexample-170623170127-thumbnail.jpg?width=120&height=120&fit=bounds" /> Digital image processing operations can be either point or group. This presentation explains both operations (point and group) and shows how convolution works by a numerical example. Ahmed Fawzy Gad ahmed.fawzy@ci.menofia.edu.eg Information Technology Department Faculty of Computers and Information (FCI) Menoufia University Egypt Find me on: AFCIT http://www.afcit.xyz YouTube https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw Google Plus https://plus.google.com/u/0/+AhmedGadIT �ݺ�ߣShare /AhmedGadFCIT LinkedIn https://www.linkedin.com/in/ahmedfgad/ ResearchGate https://www.researchgate.net/profile/Ahmed_Gad13 Academia https://www.academia.edu/ Google Scholar https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en Mendelay https://www.mendeley.com/profiles/ahmed-gad12/ ORCID https://orcid.org/0000-0003-1978-8574 StackOverFlow http://stackoverflow.com/users/5426539/ahmed-gad Twitter https://twitter.com/ahmedfgad Facebook https://www.facebook.com/ahmed.f.gadd Pinterest https://www.pinterest.com/ahmedfgad/

Operations in Digital Image Processing + Convolution by Example from Ahmed Gad

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0 MATLAB Code + Description : Real-Time Object Motion Detection and Tracking /slideshow/realtime-object-motion-detection-and-tracking/76173691 real-timeobjectmotiondetectionandtracking-170521121359
This file contains a simple description about what I have created about how to detect object motion and track whatever moving as a computer vision project when being undergraduate student at 2014. The MATLAB code of the system is also available in the document. Find me on: AFCIT http://www.afcit.xyz YouTube https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw Google Plus https://plus.google.com/u/0/+AhmedGadIT �ݺ�ߣShare /AhmedGadFCIT LinkedIn https://www.linkedin.com/in/ahmedfgad/ ResearchGate https://www.researchgate.net/profile/Ahmed_Gad13 Academia https://www.academia.edu/ Google Scholar https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en Mendelay https://www.mendeley.com/profiles/ahmed-gad12/ ORCID https://orcid.org/0000-0003-1978-8574 StackOverFlow http://stackoverflow.com/users/5426539/ahmed-gad Twitter https://twitter.com/ahmedfgad Facebook https://www.facebook.com/ahmed.f.gadd Pinterest https://www.pinterest.com/ahmedfgad/]]>
This file contains a simple description about what I have created about how to detect object motion and track whatever moving as a computer vision project when being undergraduate student at 2014. The MATLAB code of the system is also available in the document. Find me on: AFCIT http://www.afcit.xyz YouTube https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw Google Plus https://plus.google.com/u/0/+AhmedGadIT �ݺ�ߣShare /AhmedGadFCIT LinkedIn https://www.linkedin.com/in/ahmedfgad/ ResearchGate https://www.researchgate.net/profile/Ahmed_Gad13 Academia https://www.academia.edu/ Google Scholar https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en Mendelay https://www.mendeley.com/profiles/ahmed-gad12/ ORCID https://orcid.org/0000-0003-1978-8574 StackOverFlow http://stackoverflow.com/users/5426539/ahmed-gad Twitter https://twitter.com/ahmedfgad Facebook https://www.facebook.com/ahmed.f.gadd Pinterest https://www.pinterest.com/ahmedfgad/]]> Sun, 21 May 2017 12:13:59 GMT /slideshow/realtime-object-motion-detection-and-tracking/76173691 AhmedGadFCIT@slideshare.net(AhmedGadFCIT) MATLAB Code + Description : Real-Time Object Motion Detection and Tracking AhmedGadFCIT This file contains a simple description about what I have created about how to detect object motion and track whatever moving as a computer vision project when being undergraduate student at 2014. The MATLAB code of the system is also available in the document. Find me on: AFCIT http://www.afcit.xyz YouTube https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw Google Plus https://plus.google.com/u/0/+AhmedGadIT �ݺ�ߣShare /AhmedGadFCIT LinkedIn https://www.linkedin.com/in/ahmedfgad/ ResearchGate https://www.researchgate.net/profile/Ahmed_Gad13 Academia https://www.academia.edu/ Google Scholar https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en Mendelay https://www.mendeley.com/profiles/ahmed-gad12/ ORCID https://orcid.org/0000-0003-1978-8574 StackOverFlow http://stackoverflow.com/users/5426539/ahmed-gad Twitter https://twitter.com/ahmedfgad Facebook https://www.facebook.com/ahmed.f.gadd Pinterest https://www.pinterest.com/ahmedfgad/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/real-timeobjectmotiondetectionandtracking-170521121359-thumbnail.jpg?width=120&height=120&fit=bounds" /> This file contains a simple description about what I have created about how to detect object motion and track whatever moving as a computer vision project when being undergraduate student at 2014. The MATLAB code of the system is also available in the document. Find me on: AFCIT http://www.afcit.xyz YouTube https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw Google Plus https://plus.google.com/u/0/+AhmedGadIT �ݺ�ߣShare /AhmedGadFCIT LinkedIn https://www.linkedin.com/in/ahmedfgad/ ResearchGate https://www.researchgate.net/profile/Ahmed_Gad13 Academia https://www.academia.edu/ Google Scholar https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en Mendelay https://www.mendeley.com/profiles/ahmed-gad12/ ORCID https://orcid.org/0000-0003-1978-8574 StackOverFlow http://stackoverflow.com/users/5426539/ahmed-gad Twitter https://twitter.com/ahmedfgad Facebook https://www.facebook.com/ahmed.f.gadd Pinterest https://www.pinterest.com/ahmedfgad/

MATLAB Code + Description : Real-Time Object Motion Detection and Tracking from Ahmed Gad

]]> 3341 6 https://cdn.slidesharecdn.com/ss_thumbnails/real-timeobjectmotiondetectionandtracking-170521121359-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post

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0 MATLAB Code + Description : Very Simple Automatic English Optical Character Recognition (OCR) System using Artificial Neural Networks (ANNs) /slideshow/very-simple-automatic-english-optical-character-recognition-ocr-system-using-artificial-neural-networks-anns/76172708 verysimpleautomaticenglishopticalcharacterrecognitionocrusingartificialneuralnetworksanns-170521105328
This file contains a simple description about what I have created about how to recognize characters using feed forward back propagation neural network as a pattern recognition project when being undergraduate student at 2013. The MATLAB code of the system is also available in the document. Find me on: AFCIT http://www.afcit.xyz YouTube https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw Google Plus https://plus.google.com/u/0/+AhmedGadIT �ݺ�ߣShare /AhmedGadFCIT LinkedIn https://www.linkedin.com/in/ahmedfgad/ ResearchGate https://www.researchgate.net/profile/Ahmed_Gad13 Academia https://www.academia.edu/ Google Scholar https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en Mendelay https://www.mendeley.com/profiles/ahmed-gad12/ ORCID https://orcid.org/0000-0003-1978-8574 StackOverFlow http://stackoverflow.com/users/5426539/ahmed-gad Twitter https://twitter.com/ahmedfgad Facebook https://www.facebook.com/ahmed.f.gadd Pinterest https://www.pinterest.com/ahmedfgad/]]>
This file contains a simple description about what I have created about how to recognize characters using feed forward back propagation neural network as a pattern recognition project when being undergraduate student at 2013. The MATLAB code of the system is also available in the document. Find me on: AFCIT http://www.afcit.xyz YouTube https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw Google Plus https://plus.google.com/u/0/+AhmedGadIT �ݺ�ߣShare /AhmedGadFCIT LinkedIn https://www.linkedin.com/in/ahmedfgad/ ResearchGate https://www.researchgate.net/profile/Ahmed_Gad13 Academia https://www.academia.edu/ Google Scholar https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en Mendelay https://www.mendeley.com/profiles/ahmed-gad12/ ORCID https://orcid.org/0000-0003-1978-8574 StackOverFlow http://stackoverflow.com/users/5426539/ahmed-gad Twitter https://twitter.com/ahmedfgad Facebook https://www.facebook.com/ahmed.f.gadd Pinterest https://www.pinterest.com/ahmedfgad/]]> Sun, 21 May 2017 10:53:28 GMT /slideshow/very-simple-automatic-english-optical-character-recognition-ocr-system-using-artificial-neural-networks-anns/76172708 AhmedGadFCIT@slideshare.net(AhmedGadFCIT) MATLAB Code + Description : Very Simple Automatic English Optical Character Recognition (OCR) System using Artificial Neural Networks (ANNs) AhmedGadFCIT This file contains a simple description about what I have created about how to recognize characters using feed forward back propagation neural network as a pattern recognition project when being undergraduate student at 2013. The MATLAB code of the system is also available in the document. Find me on: AFCIT http://www.afcit.xyz YouTube https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw Google Plus https://plus.google.com/u/0/+AhmedGadIT �ݺ�ߣShare /AhmedGadFCIT LinkedIn https://www.linkedin.com/in/ahmedfgad/ ResearchGate https://www.researchgate.net/profile/Ahmed_Gad13 Academia https://www.academia.edu/ Google Scholar https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en Mendelay https://www.mendeley.com/profiles/ahmed-gad12/ ORCID https://orcid.org/0000-0003-1978-8574 StackOverFlow http://stackoverflow.com/users/5426539/ahmed-gad Twitter https://twitter.com/ahmedfgad Facebook https://www.facebook.com/ahmed.f.gadd Pinterest https://www.pinterest.com/ahmedfgad/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/verysimpleautomaticenglishopticalcharacterrecognitionocrusingartificialneuralnetworksanns-170521105328-thumbnail.jpg?width=120&height=120&fit=bounds" /> This file contains a simple description about what I have created about how to recognize characters using feed forward back propagation neural network as a pattern recognition project when being undergraduate student at 2013. The MATLAB code of the system is also available in the document. Find me on: AFCIT http://www.afcit.xyz YouTube https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw Google Plus https://plus.google.com/u/0/+AhmedGadIT �ݺ�ߣShare /AhmedGadFCIT LinkedIn https://www.linkedin.com/in/ahmedfgad/ ResearchGate https://www.researchgate.net/profile/Ahmed_Gad13 Academia https://www.academia.edu/ Google Scholar https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en Mendelay https://www.mendeley.com/profiles/ahmed-gad12/ ORCID https://orcid.org/0000-0003-1978-8574 StackOverFlow http://stackoverflow.com/users/5426539/ahmed-gad Twitter https://twitter.com/ahmedfgad Facebook https://www.facebook.com/ahmed.f.gadd Pinterest https://www.pinterest.com/ahmedfgad/

MATLAB Code + Description : Very Simple Automatic English Optical Character Recognition (OCR) System using Artificial Neural Networks (ANNs) from Ahmed Gad

]]> 2079 3 https://cdn.slidesharecdn.com/ss_thumbnails/verysimpleautomaticenglishopticalcharacterrecognitionocrusingartificialneuralnetworksanns-170521105328-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post

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0 Graduation Project - Face Login : A Robust Face Identification System for Security-Based Services /slideshow/graduation-project-face-login-a-robust-face-identification-system-for-securitybased-services/76161820 faceloginarobustfaceidentificationsystemforsecurity-basedservices-170520182420
Face login is my 2015 graduation project started in 2014 and lasted 1.5 years of work. Generally, it is an identification system using face images. It is a multi-use system but it was mainly created to authorize users to login into their system. There is an IEEE paper published by the project algorithm used in ICCES 2014 http://ieeexplore.ieee.org/abstract/document/7030929/. Here is its citation Semary, Noura A., and Ahmed Fawzi Gad. "A proposed framework for robust face identification system." Computer Engineering & Systems (ICCES), 2014 9th International Conference on. IEEE, 2014. A YouTube video describing the project generally. https://www.youtube.com/watch?v=OUvaPW70Eko Find me on: AFCIT http://www.afcit.xyz YouTube https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw Google Plus https://plus.google.com/u/0/+AhmedGadIT �ݺ�ߣShare /AhmedGadFCIT LinkedIn https://www.linkedin.com/in/ahmedfgad/ ResearchGate https://www.researchgate.net/profile/Ahmed_Gad13 Academia https://www.academia.edu/ Google Scholar https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en Mendelay https://www.mendeley.com/profiles/ahmed-gad12/ ORCID https://orcid.org/0000-0003-1978-8574 StackOverFlow http://stackoverflow.com/users/5426539/ahmed-gad Twitter https://twitter.com/ahmedfgad Facebook https://www.facebook.com/ahmed.f.gadd Pinterest https://www.pinterest.com/ahmedfgad/]]>
Face login is my 2015 graduation project started in 2014 and lasted 1.5 years of work. Generally, it is an identification system using face images. It is a multi-use system but it was mainly created to authorize users to login into their system. There is an IEEE paper published by the project algorithm used in ICCES 2014 http://ieeexplore.ieee.org/abstract/document/7030929/. Here is its citation Semary, Noura A., and Ahmed Fawzi Gad. "A proposed framework for robust face identification system." Computer Engineering & Systems (ICCES), 2014 9th International Conference on. IEEE, 2014. A YouTube video describing the project generally. https://www.youtube.com/watch?v=OUvaPW70Eko Find me on: AFCIT http://www.afcit.xyz YouTube https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw Google Plus https://plus.google.com/u/0/+AhmedGadIT �ݺ�ߣShare /AhmedGadFCIT LinkedIn https://www.linkedin.com/in/ahmedfgad/ ResearchGate https://www.researchgate.net/profile/Ahmed_Gad13 Academia https://www.academia.edu/ Google Scholar https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en Mendelay https://www.mendeley.com/profiles/ahmed-gad12/ ORCID https://orcid.org/0000-0003-1978-8574 StackOverFlow http://stackoverflow.com/users/5426539/ahmed-gad Twitter https://twitter.com/ahmedfgad Facebook https://www.facebook.com/ahmed.f.gadd Pinterest https://www.pinterest.com/ahmedfgad/]]> Sat, 20 May 2017 18:24:20 GMT /slideshow/graduation-project-face-login-a-robust-face-identification-system-for-securitybased-services/76161820 AhmedGadFCIT@slideshare.net(AhmedGadFCIT) Graduation Project - Face Login : A Robust Face Identification System for Security-Based Services AhmedGadFCIT Face login is my 2015 graduation project started in 2014 and lasted 1.5 years of work. Generally, it is an identification system using face images. It is a multi-use system but it was mainly created to authorize users to login into their system. There is an IEEE paper published by the project algorithm used in ICCES 2014 http://ieeexplore.ieee.org/abstract/document/7030929/. Here is its citation Semary, Noura A., and Ahmed Fawzi Gad. "A proposed framework for robust face identification system." Computer Engineering & Systems (ICCES), 2014 9th International Conference on. IEEE, 2014. A YouTube video describing the project generally. https://www.youtube.com/watch?v=OUvaPW70Eko Find me on: AFCIT http://www.afcit.xyz YouTube https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw Google Plus https://plus.google.com/u/0/+AhmedGadIT �ݺ�ߣShare /AhmedGadFCIT LinkedIn https://www.linkedin.com/in/ahmedfgad/ ResearchGate https://www.researchgate.net/profile/Ahmed_Gad13 Academia https://www.academia.edu/ Google Scholar https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en Mendelay https://www.mendeley.com/profiles/ahmed-gad12/ ORCID https://orcid.org/0000-0003-1978-8574 StackOverFlow http://stackoverflow.com/users/5426539/ahmed-gad Twitter https://twitter.com/ahmedfgad Facebook https://www.facebook.com/ahmed.f.gadd Pinterest https://www.pinterest.com/ahmedfgad/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/faceloginarobustfaceidentificationsystemforsecurity-basedservices-170520182420-thumbnail.jpg?width=120&height=120&fit=bounds" /> Face login is my 2015 graduation project started in 2014 and lasted 1.5 years of work. Generally, it is an identification system using face images. It is a multi-use system but it was mainly created to authorize users to login into their system. There is an IEEE paper published by the project algorithm used in ICCES 2014 http://ieeexplore.ieee.org/abstract/document/7030929/. Here is its citation Semary, Noura A., and Ahmed Fawzi Gad. "A proposed framework for robust face identification system." Computer Engineering & Systems (ICCES), 2014 9th International Conference on. IEEE, 2014. A YouTube video describing the project generally. https://www.youtube.com/watch?v=OUvaPW70Eko Find me on: AFCIT http://www.afcit.xyz YouTube https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw Google Plus https://plus.google.com/u/0/+AhmedGadIT �ݺ�ߣShare /AhmedGadFCIT LinkedIn https://www.linkedin.com/in/ahmedfgad/ ResearchGate https://www.researchgate.net/profile/Ahmed_Gad13 Academia https://www.academia.edu/ Google Scholar https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en Mendelay https://www.mendeley.com/profiles/ahmed-gad12/ ORCID https://orcid.org/0000-0003-1978-8574 StackOverFlow http://stackoverflow.com/users/5426539/ahmed-gad Twitter https://twitter.com/ahmedfgad Facebook https://www.facebook.com/ahmed.f.gadd Pinterest https://www.pinterest.com/ahmedfgad/

Graduation Project - Face Login : A Robust Face Identification System for Security-Based Services from Ahmed Gad

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0 https://cdn.slidesharecdn.com/profile-photo-AhmedGadFCIT-48x48.jpg?cb=1718827436 A researcher in machine learning and creator of PyGAD https://pygad.readthedocs.io. For more information: https://www.kdnuggets.com/author/ahmed-gad https://towardsdatascience.com/@ahmedfgad https://github.com/ahmedfgad www.linkedin.com/in/ahmedfgad https://cdn.slidesharecdn.com/ss_thumbnails/iceitahmedgadfatimaezzahracython-200718172026-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/iceit20-cython-for-speedingup-genetic-algorithm/237030502 ICEIT'20 Cython for Sp... https://cdn.slidesharecdn.com/ss_thumbnails/ahmedgad-itce-npca-190205192615-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/numpycnnandroid-a-library-for-straightforward-implementation-of-convolutional-neural-networks-for-android-devices-itce-2019/130623924 NumPyCNNAndroid: A Lib... https://cdn.slidesharecdn.com/ss_thumbnails/pythonforcomputervision-180920221349-thumbnail.jpg?width=320&height=320&fit=bounds AhmedGadFCIT/python-for-computer-vision-revision-2nd-edition Python for Computer Vi...