This document summarizes a project to classify handwritten digits from the MNIST dataset using a decision tree strategy. It discusses using decision trees to determine informative features and construct a classifier from 60,000 training examples and 10,000 test examples. The implementation loads data, trains on 21,000 items, tests on the remaining items, and displays predictions with 28x28 pixel images. Accuracy is improved further with a nearest neighbor final test, achieving 99.6% classification rate. Screenshots of the running code are provided in an attached folder.
This document outlines the agenda for a machine learning programming course lecture on logistic regression and multiclass classification. The lecture will cover model validation using training and test sets, recognizing single digits from the MNIST dataset, preprocessing and encoding image data, and extending linear regression to logistic regression for multiclass classification problems. It provides examples of assessing model performance on training versus test data to avoid overfitting. The goal of the lab session is to build a program classifying MNIST digit images into 10 classes using logistic regression.
This document outlines the agenda for a machine learning programming course lecture on logistic regression and multiclass classification. The lecture will cover model validation using training and test sets, recognizing single digits from the MNIST dataset, preprocessing and encoding image data, and extending linear regression to logistic regression for multiclass classification problems. It provides examples of assessing model performance on training versus test data to avoid overfitting. The goal of the lab session is to build a program classifying MNIST digit images into 10 classes using logistic regression.
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
The document describes building a convolutional neural network (CNN) model to classify handwritten digits from the MNIST dataset. It discusses collecting MNIST image and label data, developing a CNN using Keras with TensorFlow that includes convolutional and pooling layers followed by dense layers, training the model for 10 epochs using 5-fold cross-validation, evaluating the trained model on the test set where it achieved 98.82% accuracy, and concluding the CNN can successfully recognize handwritten digits.
Evaluating Machine Learning Algorithms for Materials Science using the Matben...Anubhav Jain
1) The document discusses evaluating machine learning algorithms for materials science using the Matbench protocol.
2) Matbench provides standardized datasets, testing procedures, and an online leaderboard to benchmark and compare machine learning performance.
3) This allows different groups to evaluate algorithms independently and identify best practices for materials science predictions.
Teach a neural network to read handwritingVipul Kaushal
This document discusses teaching a neural network to read handwritten digits using the MNIST dataset. It uses a deep convolutional neural network with convolutional layers to extract features from images, max pooling to enhance dominant features, flatten and dense layers, and softmax activation. The model is compiled and trained using the Adam optimizer on 60,000 training images over multiple epochs, and is tested on 10,000 testing images to classify handwritten digits. Problems in choosing the architecture and loading the MNIST format dataset were addressed by referring to cited articles and resources.
A Large-scale hierarchical image databaseRezapourabbas
The digital era has brought with it an enormous explosion of data.
More robust models and algorithms can be proposed by exploiting these images.
How such data can be utilized and organized ?
In this paper, introduce a new image database called “ImageNet”.
This document summarizes a project to classify handwritten digits from the MNIST dataset using a decision tree strategy. It discusses using decision trees to determine informative features and construct a classifier from 60,000 training examples and 10,000 test examples. The implementation loads data, trains on 21,000 items, tests on the remaining items, and displays predictions with 28x28 pixel images. Accuracy is improved further with a nearest neighbor final test, achieving 99.6% classification rate. Screenshots of the running code are provided in an attached folder.
This document outlines the agenda for a machine learning programming course lecture on logistic regression and multiclass classification. The lecture will cover model validation using training and test sets, recognizing single digits from the MNIST dataset, preprocessing and encoding image data, and extending linear regression to logistic regression for multiclass classification problems. It provides examples of assessing model performance on training versus test data to avoid overfitting. The goal of the lab session is to build a program classifying MNIST digit images into 10 classes using logistic regression.
This document outlines the agenda for a machine learning programming course lecture on logistic regression and multiclass classification. The lecture will cover model validation using training and test sets, recognizing single digits from the MNIST dataset, preprocessing and encoding image data, and extending linear regression to logistic regression for multiclass classification problems. It provides examples of assessing model performance on training versus test data to avoid overfitting. The goal of the lab session is to build a program classifying MNIST digit images into 10 classes using logistic regression.
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
The document describes building a convolutional neural network (CNN) model to classify handwritten digits from the MNIST dataset. It discusses collecting MNIST image and label data, developing a CNN using Keras with TensorFlow that includes convolutional and pooling layers followed by dense layers, training the model for 10 epochs using 5-fold cross-validation, evaluating the trained model on the test set where it achieved 98.82% accuracy, and concluding the CNN can successfully recognize handwritten digits.
Evaluating Machine Learning Algorithms for Materials Science using the Matben...Anubhav Jain
1) The document discusses evaluating machine learning algorithms for materials science using the Matbench protocol.
2) Matbench provides standardized datasets, testing procedures, and an online leaderboard to benchmark and compare machine learning performance.
3) This allows different groups to evaluate algorithms independently and identify best practices for materials science predictions.
Teach a neural network to read handwritingVipul Kaushal
This document discusses teaching a neural network to read handwritten digits using the MNIST dataset. It uses a deep convolutional neural network with convolutional layers to extract features from images, max pooling to enhance dominant features, flatten and dense layers, and softmax activation. The model is compiled and trained using the Adam optimizer on 60,000 training images over multiple epochs, and is tested on 10,000 testing images to classify handwritten digits. Problems in choosing the architecture and loading the MNIST format dataset were addressed by referring to cited articles and resources.
A Large-scale hierarchical image databaseRezapourabbas
The digital era has brought with it an enormous explosion of data.
More robust models and algorithms can be proposed by exploiting these images.
How such data can be utilized and organized ?
In this paper, introduce a new image database called “ImageNet”.
The document describes developing a model to predict house prices using deep learning techniques. It proposes using a dataset with house features without labels and applying regression algorithms like K-nearest neighbors, support vector machine, and artificial neural networks. The models are trained and tested on split data, with the artificial neural network achieving the lowest mean absolute percentage error of 18.3%, indicating it is the most accurate model for predicting house prices based on the data.
Dmitry will show the audience on how get started with Mxnet and building Deep Learning models to classify images, sound and text.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
CNN FEATURES ARE ALSO GREAT AT UNSUPERVISED CLASSIFICATION cscpconf
This paper aims at providing insight on the transferability of deep CNN features to
unsupervised problems. We study the impact of different pretrained CNN feature extractors on
the problem of image set clustering for object classification as well as fine-grained
classification. We propose a rather straightforward pipeline combining deep-feature extraction
using a CNN pretrained on ImageNet and a classic clustering algorithm to classify sets of
images. This approach is compared to state-of-the-art algorithms in image-clustering and
provides better results. These results strengthen the belief that supervised training of deep CNN
on large datasets, with a large variability of classes, extracts better features than most carefully
designed engineering approaches, even for unsupervised tasks. We also validate our approach
on a robotic application, consisting in sorting and storing objects smartly based on clustering
Image Segmentation Using Deep Learning : A surveyNUPUR YADAV
1. The document discusses various deep learning models for image segmentation, including fully convolutional networks, encoder-decoder models, multi-scale pyramid networks, and dilated convolutional models.
2. It provides details on popular architectures like U-Net, SegNet, and models from the DeepLab family.
3. The document also reviews datasets commonly used to evaluate image segmentation methods and reports accuracies of different models on the Cityscapes dataset.
Build a simple image recognition system with tensor flowDebasisMohanty37
A perfect working model to detect mnist dataset using TensorFlow.
Dataset:
http://yann.lecun.com/exdb/mnist/
For code check the below GitHub links:
https://github.com/Jitudebz/psychic-pancake
Course Information
• Nisa Trainings
• IBM Netezza Online Training
• Duration: 30 Hours
• Timings: Weekdays (1-2 Hours per day) [OR] Weekends (2-3 Hours per day)
• Training Method: Instructor Led Online One-on-One Live Interactive Sessions.
The document describes a study that used a convolutional neural network with a ConvNeXtLarge architecture to classify skin cancer images into benign and malignant classes. The CNN model was trained on a dataset of 3,297 skin cancer images from Kaggle. It achieved an AUC of 0.91 for classifying the images, demonstrating the ConvNeXtLarge architecture is effective for this task. The study aims to help early diagnosis and treatment of skin cancers.
This document discusses a method for handwritten character recognition using a K-nearest neighbors (K-NN) classification algorithm. It begins by introducing the problem of handwritten character recognition and the challenges involved. It then describes the main steps of the proposed method: preprocessing the image data, extracting features, and classifying characters using K-NN. The document tests the method on the MNIST dataset of handwritten digits, achieving an accuracy of 97.67%. It concludes that the method is able to accurately recognize handwritten characters independently of size, font, or writer style.
HANDWRITTEN DIGIT RECOGNITION SYSTEM BASED ON CNN AND SVMsipij
The recognition of handwritten digits has aroused the interest of the scientific community and is the subject
of a large number of research works thanks to its various applications. The objective of this paper is to
develop a system capable of recognizing handwritten digits using a convolutional neural network (CNN)
combined with machine learning approaches to ensure diversity in automatic classification tools. In this
work, we propose a classification method based on deep learning, in particular the convolutional neural
network for feature extraction, it is a powerful tool that has had great success in image classification,
followed by the support vector machine (SVM) for higher performance. We used the dataset (MNIST), and
the results obtained showed that the combination of CNN with SVM improves the performance of the model
as well as the classification accuracy with a rate of 99.12%.
Handwritten Digit Recognition System based on CNN and SVMsipij
This document summarizes a research paper on developing a handwritten digit recognition system using a combination of convolutional neural networks (CNN) and support vector machines (SVM). The proposed system extracts features from images using a CNN, then classifies the images using an SVM. The system was tested on the MNIST dataset and achieved 99.12% classification accuracy, outperforming a basic CNN-only model. This high accuracy demonstrates that combining CNNs with SVMs improves performance for handwritten digit recognition.
Improving Graph Based Model for Content Based Image RetrievalIRJET Journal
This document summarizes a research paper that proposes improvements to a graph-based model called Manifold Ranking (MR) for content-based image retrieval. Specifically, it introduces a novel scalable graph-based ranking model called Efficient Manifold Ranking (EMR) that addresses shortcomings of MR in scalable graph construction and efficient ranking computation. The proposed EMR model builds an anchor graph on the database instead of a traditional k-nearest neighbor graph, and designs a new form of adjacency matrix to speed up the ranking computation. Experimental results on large image databases demonstrate that EMR is effective for real-world image retrieval applications.
Sample Codes: https://github.com/davegautam/dotnetconfsamplecodes
Presentation on How you can get started with ML.NET. If you are existing .NET Stack Developer and Wanna use the same technology into Machine Learning, this slide focuses on how you can use ML.NET for Machine Learning.
This document describes a project to implement real-time facial recognition using OpenCV and Python. The project uses a laptop's webcam to capture video frames and detect and recognize faces in each frame. It trains an image dataset with face images and IDs then detects faces in each new video frame. It predicts faces by comparing features to the training data and labels matches based on a confidence level threshold. The document outlines the use of Haar cascade classifiers, LBPH algorithms, and OpenCV functions to complete the facial recognition process in real-time on new video frames from the webcam.
This document describes a project to implement real-time facial recognition using OpenCV and Python. The project uses a laptop's webcam to capture video frames and detect and recognize faces in each frame. It trains an image dataset with face images and IDs then detects faces in each new video frame. It predicts faces by comparing features to the training data and labels matches based on a confidence level threshold. The document outlines the use of Haar cascade classifiers, LBPH algorithms, and OpenCV functions to complete the facial recognition process in real-time on new video frames from the webcam.
Deep Learning with Apache Spark: an IntroductionEmanuele Bezzi
This document introduces deep learning with Apache Spark. It discusses machine learning and deep learning concepts like perceptrons, neural networks, supervised learning and gradient descent. It then explains how Apache Spark can be used to distribute deep learning training by sharding data and model replicas across worker nodes. An example uses Spark and Deeplearning4j to perform distributed training of a convolutional neural network on the MNIST dataset to classify handwritten digits. The network is trained over multiple epochs and evaluated on a test set, achieving over 95% accuracy.
Generating Illustrative Snippets for Open Data on the WebGong Cheng
We propose generating illustrative snippets from datasets to serve with metadata on dataset search engines. Currently, only metadata is shown. Snippets would help users understand the contents faster by covering important types and entities, using familiar entities, and keeping entities related. We formulate the snippet generation as a maximum-weight-and-coverage connected graph problem to optimize for these qualities. Experimental results show our snippets outperform baselines.
This document discusses using machine learning approaches to accurately detect, predict, segment and classify tumors from medical image data. It provides an overview of supervised learning and classification techniques like support vector machines, K-nearest neighbors, and decision trees. It notes that deep learning algorithms like convolutional neural networks have shown promising performance in medical domains. The objective is to develop a system that can analyze medical image data to detect cancer. It tests various classifiers on a dataset from the UCI repository containing 57 features from 32 instances, obtaining an accuracy of 42.857% using a support vector machine classifier.
The document discusses using predictive data mining techniques to improve customer retention and development. It outlines the workflow used which includes data cleaning, clustering customers using K-Means and MST-based clustering, making classification models for each cluster to predict churn, and developing strategies to retain and develop customers. The results show the clusters formed, classification accuracies between Naive Bayes and KNN algorithms, and the final hybrid model using MST clustering and Naive Bayes classification achieving the best performance.
This document provides information about Mohan C R, including his education and qualifications, skills, projects, publications and awards. He has over 2 years of experience as a data scientist and machine learning engineer. He has a B.Tech in electronics and communication engineering as well as nanodegree certificates in data science and machine learning foundations from Udacity. His skills include Python, SQL, AWS, TensorFlow and he has worked on projects involving image classification, recommendation engines, disaster response pipelines and customer segmentation.
Mounika Gottumukkala is seeking a position that allows her to utilize her skills in a challenging environment and offers professional growth. She has a Bachelor's degree in Engineering and an international certification in software testing. Her technical skills include programming languages like C and Java, testing tools like QTP, databases like SQL Server, and packages like MS Office. For her certification project, she tested an online banking system, creating test cases and scripts. She also worked on engineering projects involving image processing and serial communication. Her personal attributes include leadership, teamwork, problem solving, and the ability to learn quickly.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
More Related Content
Similar to Mnist dataset_Image Classification using Opencv.pptx
The document describes developing a model to predict house prices using deep learning techniques. It proposes using a dataset with house features without labels and applying regression algorithms like K-nearest neighbors, support vector machine, and artificial neural networks. The models are trained and tested on split data, with the artificial neural network achieving the lowest mean absolute percentage error of 18.3%, indicating it is the most accurate model for predicting house prices based on the data.
Dmitry will show the audience on how get started with Mxnet and building Deep Learning models to classify images, sound and text.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
CNN FEATURES ARE ALSO GREAT AT UNSUPERVISED CLASSIFICATION cscpconf
This paper aims at providing insight on the transferability of deep CNN features to
unsupervised problems. We study the impact of different pretrained CNN feature extractors on
the problem of image set clustering for object classification as well as fine-grained
classification. We propose a rather straightforward pipeline combining deep-feature extraction
using a CNN pretrained on ImageNet and a classic clustering algorithm to classify sets of
images. This approach is compared to state-of-the-art algorithms in image-clustering and
provides better results. These results strengthen the belief that supervised training of deep CNN
on large datasets, with a large variability of classes, extracts better features than most carefully
designed engineering approaches, even for unsupervised tasks. We also validate our approach
on a robotic application, consisting in sorting and storing objects smartly based on clustering
Image Segmentation Using Deep Learning : A surveyNUPUR YADAV
1. The document discusses various deep learning models for image segmentation, including fully convolutional networks, encoder-decoder models, multi-scale pyramid networks, and dilated convolutional models.
2. It provides details on popular architectures like U-Net, SegNet, and models from the DeepLab family.
3. The document also reviews datasets commonly used to evaluate image segmentation methods and reports accuracies of different models on the Cityscapes dataset.
Build a simple image recognition system with tensor flowDebasisMohanty37
A perfect working model to detect mnist dataset using TensorFlow.
Dataset:
http://yann.lecun.com/exdb/mnist/
For code check the below GitHub links:
https://github.com/Jitudebz/psychic-pancake
Course Information
• Nisa Trainings
• IBM Netezza Online Training
• Duration: 30 Hours
• Timings: Weekdays (1-2 Hours per day) [OR] Weekends (2-3 Hours per day)
• Training Method: Instructor Led Online One-on-One Live Interactive Sessions.
The document describes a study that used a convolutional neural network with a ConvNeXtLarge architecture to classify skin cancer images into benign and malignant classes. The CNN model was trained on a dataset of 3,297 skin cancer images from Kaggle. It achieved an AUC of 0.91 for classifying the images, demonstrating the ConvNeXtLarge architecture is effective for this task. The study aims to help early diagnosis and treatment of skin cancers.
This document discusses a method for handwritten character recognition using a K-nearest neighbors (K-NN) classification algorithm. It begins by introducing the problem of handwritten character recognition and the challenges involved. It then describes the main steps of the proposed method: preprocessing the image data, extracting features, and classifying characters using K-NN. The document tests the method on the MNIST dataset of handwritten digits, achieving an accuracy of 97.67%. It concludes that the method is able to accurately recognize handwritten characters independently of size, font, or writer style.
HANDWRITTEN DIGIT RECOGNITION SYSTEM BASED ON CNN AND SVMsipij
The recognition of handwritten digits has aroused the interest of the scientific community and is the subject
of a large number of research works thanks to its various applications. The objective of this paper is to
develop a system capable of recognizing handwritten digits using a convolutional neural network (CNN)
combined with machine learning approaches to ensure diversity in automatic classification tools. In this
work, we propose a classification method based on deep learning, in particular the convolutional neural
network for feature extraction, it is a powerful tool that has had great success in image classification,
followed by the support vector machine (SVM) for higher performance. We used the dataset (MNIST), and
the results obtained showed that the combination of CNN with SVM improves the performance of the model
as well as the classification accuracy with a rate of 99.12%.
Handwritten Digit Recognition System based on CNN and SVMsipij
This document summarizes a research paper on developing a handwritten digit recognition system using a combination of convolutional neural networks (CNN) and support vector machines (SVM). The proposed system extracts features from images using a CNN, then classifies the images using an SVM. The system was tested on the MNIST dataset and achieved 99.12% classification accuracy, outperforming a basic CNN-only model. This high accuracy demonstrates that combining CNNs with SVMs improves performance for handwritten digit recognition.
Improving Graph Based Model for Content Based Image RetrievalIRJET Journal
This document summarizes a research paper that proposes improvements to a graph-based model called Manifold Ranking (MR) for content-based image retrieval. Specifically, it introduces a novel scalable graph-based ranking model called Efficient Manifold Ranking (EMR) that addresses shortcomings of MR in scalable graph construction and efficient ranking computation. The proposed EMR model builds an anchor graph on the database instead of a traditional k-nearest neighbor graph, and designs a new form of adjacency matrix to speed up the ranking computation. Experimental results on large image databases demonstrate that EMR is effective for real-world image retrieval applications.
Sample Codes: https://github.com/davegautam/dotnetconfsamplecodes
Presentation on How you can get started with ML.NET. If you are existing .NET Stack Developer and Wanna use the same technology into Machine Learning, this slide focuses on how you can use ML.NET for Machine Learning.
This document describes a project to implement real-time facial recognition using OpenCV and Python. The project uses a laptop's webcam to capture video frames and detect and recognize faces in each frame. It trains an image dataset with face images and IDs then detects faces in each new video frame. It predicts faces by comparing features to the training data and labels matches based on a confidence level threshold. The document outlines the use of Haar cascade classifiers, LBPH algorithms, and OpenCV functions to complete the facial recognition process in real-time on new video frames from the webcam.
This document describes a project to implement real-time facial recognition using OpenCV and Python. The project uses a laptop's webcam to capture video frames and detect and recognize faces in each frame. It trains an image dataset with face images and IDs then detects faces in each new video frame. It predicts faces by comparing features to the training data and labels matches based on a confidence level threshold. The document outlines the use of Haar cascade classifiers, LBPH algorithms, and OpenCV functions to complete the facial recognition process in real-time on new video frames from the webcam.
Deep Learning with Apache Spark: an IntroductionEmanuele Bezzi
This document introduces deep learning with Apache Spark. It discusses machine learning and deep learning concepts like perceptrons, neural networks, supervised learning and gradient descent. It then explains how Apache Spark can be used to distribute deep learning training by sharding data and model replicas across worker nodes. An example uses Spark and Deeplearning4j to perform distributed training of a convolutional neural network on the MNIST dataset to classify handwritten digits. The network is trained over multiple epochs and evaluated on a test set, achieving over 95% accuracy.
Generating Illustrative Snippets for Open Data on the WebGong Cheng
We propose generating illustrative snippets from datasets to serve with metadata on dataset search engines. Currently, only metadata is shown. Snippets would help users understand the contents faster by covering important types and entities, using familiar entities, and keeping entities related. We formulate the snippet generation as a maximum-weight-and-coverage connected graph problem to optimize for these qualities. Experimental results show our snippets outperform baselines.
This document discusses using machine learning approaches to accurately detect, predict, segment and classify tumors from medical image data. It provides an overview of supervised learning and classification techniques like support vector machines, K-nearest neighbors, and decision trees. It notes that deep learning algorithms like convolutional neural networks have shown promising performance in medical domains. The objective is to develop a system that can analyze medical image data to detect cancer. It tests various classifiers on a dataset from the UCI repository containing 57 features from 32 instances, obtaining an accuracy of 42.857% using a support vector machine classifier.
The document discusses using predictive data mining techniques to improve customer retention and development. It outlines the workflow used which includes data cleaning, clustering customers using K-Means and MST-based clustering, making classification models for each cluster to predict churn, and developing strategies to retain and develop customers. The results show the clusters formed, classification accuracies between Naive Bayes and KNN algorithms, and the final hybrid model using MST clustering and Naive Bayes classification achieving the best performance.
This document provides information about Mohan C R, including his education and qualifications, skills, projects, publications and awards. He has over 2 years of experience as a data scientist and machine learning engineer. He has a B.Tech in electronics and communication engineering as well as nanodegree certificates in data science and machine learning foundations from Udacity. His skills include Python, SQL, AWS, TensorFlow and he has worked on projects involving image classification, recommendation engines, disaster response pipelines and customer segmentation.
Mounika Gottumukkala is seeking a position that allows her to utilize her skills in a challenging environment and offers professional growth. She has a Bachelor's degree in Engineering and an international certification in software testing. Her technical skills include programming languages like C and Java, testing tools like QTP, databases like SQL Server, and packages like MS Office. For her certification project, she tested an online banking system, creating test cases and scripts. She also worked on engineering projects involving image processing and serial communication. Her personal attributes include leadership, teamwork, problem solving, and the ability to learn quickly.
Similar to Mnist dataset_Image Classification using Opencv.pptx (20)
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
20 Comprehensive Checklist of Designing and Developing a WebsitePixlogix Infotech
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
2. Dataset info:
Dataset loaded using keras library namely
mnist dataset.
• Problem Statement:
This project aims to develop an
accurate image classification
system using OpenCV to categorize
unseen images into predefined
classes, potentially leveraging
deep learning for improved
performance. The challenge lies in
balancing data acquisition,
processing speed for real-time
applications (if applicable), and
achieving a desired level of
classification accuracy.
3. Data Introduction:
It consists of a large collection of grayscale images
of handwritten digits (0 through 9), each
measuring 28 pixels by 28 pixels. Originally
constructed from scanned documents, MNIST has
become a standard dataset.
Data Preprocessing:
I employed OpenCV functionalities for tasks such
as transforming the data type, reshaping and
normalization to prepare the MNIST dataset for
analysis.
6. Model Training:
Implemented machine learning
models, leveraging OpenCV's
integration with frameworks like
Support vector classifier to classify
handwritten digits with high accuracy.
Evaluation and Results:
Evaluated the performance of the
trained models using appropriate
metrics and approximate accuracy is
98 percent wit recall and precision of
almost 99 percent and 98 percent.
7. Correctly classified class
by model
Incorrectly classified class
by model
There is total 10,000 classes data in testing phase and model predicted
9833 classes correctly.