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Advancement of Data Processing Methods for Artificial and Computing Intelligence https://bit.ly/3Uukx2C is applicable to a wide range of data that contribute to data science concerns, and can be used to promote research in this high-potential field. As a result of the exponential growth of data in recent years, the combined notions of big data and AI have given rise to many study areas, such as scale-up behavior from classical algorithms. Editors: Seema Rawat, Amity University. V. Ajantha Devi, AP3 Solutions. Praveen Kumar,Amity University. #datascience #deeplearning #dataprocessing #artificialintelligence #dataanalytics #BigData #sentimentanalysis #Artificialneuralnetworks #bigdataanalytics #dataengineering #machinelearning #deeplearning and its Applications, #PredictiveAnalytics #DataDrivenAnalytics , and #businessmanagement

Advancement of Data Processing Methods for Artificial and Computing Intelligence https://bit.ly/3Uukx2C is applicable to a wide range of data that contribute to data science concerns, and can be used to promote research in this high-potential field. As a result of the exponential growth of data in recent years, the combined notions of big data and AI have given rise to many study areas, such as scale-up behaviour from classical algorithms. Editors: Seema Rawat, Amity University. V. Ajantha Devi, AP3 Solutions. Praveen Kumar,Amity University. #datascience #deeplearning #dataprocessing #artificialintelligence #dataanalytics #BigData #sentimentanalysis #Artificialneuralnetworks #bigdataanalytics #dataengineering #machinelearning #deeplearning and its Applications, #PredictiveAnalytics #DataDrivenAnalytics , and #businessmanagement

Advancement of Data Processing Methods for Artificial and Computing Intelligence https://bit.ly/3Uukx2C is applicable to a wide range of data that contribute to data science concerns, and can be used to promote research in this high-potential field. As a result of the exponential growth of data in recent years, the combined notions of big data and AI have given rise to many study areas, such as scale-up behaviour from classical algorithms. Editors: Dr. Seema Rawat, Amity University. V. Ajantha Devi, AP3 Solutions. Praveen Kumar, Amity University. #datascience #deeplearning #dataprocessing #artificialintelligence #dataanalytics #BigData #sentimentanalysis #Artificialneuralnetworks #bigdataanalytics #dataengineering #machinelearning #deeplearning and its Applications, #PredictiveAnalytics #DataDrivenAnalytics , and #businessmanagement

"This synthetic data must meet two requirements: 1️⃣ First, it must somewhat resemble the original data statistically, to ensure realism and keep problems engaging for data scientists. 2️⃣ Second, it must also formally and structurally resemble the original data, so that any software written on top of it can be reused. In order to meet these requirements, the data must be statistically modeled in its original form, so that we can sample from and recreate it. In our case and in most cases, that form is the database itself. Thus, modeling must occur before any transformations and aggregations are applied." From the paper "The Synthetic data vault" from 2016 whose camera ready version was submitted #otd in 2016 from Massachusetts Institute of Technology Today, #sdv counts millions of downloads, thousands of users and so many additional modules have been added to evaluate #syntheticdata, #benchmark models and so much more.. You can find the original paper here: https://lnkd.in/evSmnZz8 #syntheticdata, #generativeai, #tabulardata , #ai, #machinelearning, #datascience ---- Neha Patki Roy Wedge and Kalyan Veeramachaneni, MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), MIT Laboratory for Information and Decision Systems (LIDS) MIT Schwarzman College of Computing MIT Data-to-AI Lab

Data structures are crucial in computer science, offering efficient ways to organize and store data, which is essential for optimal algorithm performance. They ensure quick data access, modification, and deletion, which is vital for performance-critical applications. For instance, hash tables significantly reduce search time complexity compared to linked lists. Data structures like trees and graphs 🌳📊 are pivotal in network analysis, AI 🤖, and database indexing. Stacks and queues 📚📥 effectively manage data for processes like recursion and task scheduling in operating systems. Choosing the right data structure impacts memory usage 💾, minimizing overhead in resource-limited environments. Understanding them enhances problem-solving skills 🧠 by providing tools for diverse computational problems. They are foundational in advanced algorithms and crucial in fields like machine learning 🧬, where data organization directly affects model effectiveness. In summary, data structures are vital for efficient data management 📊, optimal performance 🚀, and effective problem-solving in computer science. #DataStructures #ComputerScience #AlgorithmOptimization #TechInnovation #MachineLearning #AI #SoftwareDevelopment #PerformanceEngineering #MemoryManagement #ProblemSolving #TechTrends

In the ever-evolving landscape of data science, the generation of synthetic data has emerged as a vital technique for augmenting datasets, particularly when dealing with sensitive or limited data. Enter the Copula GAN Synthesizer, an innovative tool that blends classical statistical methods with GAN-based deep learning techniques to train models and generate high-quality synthetic data. Key Benefits: •⁠ ⁠Versatility in handling diverse data types. •⁠ ⁠Efficiently manages datasets with missing values. •⁠ ⁠Ensures data privacy while generating realistic synthetic data. At Bluescarf Artificial Intelligence Limited., we're harnessing this technology to enhance data-driven decision-making and innovation across various projects. Stay tuned for more insights on how we are leveraging advanced technologies to drive progress and safeguard data integrity. #SyntheticData #GAN #DataScience #MachineLearning #Bluescarf #Innovation #TechTrends

Following the previous guide on modern methods in computer vision, I am delighted to present the next installment, "Comprehensive Guide to Classical Methods in Computer Vision for Data Science." This guide delves into the foundational techniques that have shaped the field of computer vision, offering detailed explanations, examples, advantages, disadvantages, practical applications and strategic insights. 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬 𝐈𝐧𝐜𝐥𝐮𝐝𝐞: ▫ Edge Detection ▫ Feature Detection and Matching ▫ Image Segmentation ▫ Image Transformations ▫ Object Tracking ▫ Morphological Operations ▫ Template Matching ▫ Histogram of Oriented Gradients (HOG) ▫ Affine Transformations ▫ K-Means Clustering ▫ Pyramid Representation ▫ Random Sample Consensus (RANSAC) 𝐖𝐡𝐞𝐧 𝐭𝐨 𝐔𝐬𝐞 𝐂𝐥𝐚𝐬𝐬𝐢𝐜𝐚𝐥 𝐌𝐞𝐭𝐡𝐨𝐝𝐬: These methods are suitable for simpler tasks such as straightforward object detection and shape detection, or when computational resources are limited. These techniques are often easier to implement and understand, making them suitable for quick solutions and small datasets (typically fewer than 10,000 images). They remain highly relevant for various applications such as shape detection, object recognition, image segmentation, and more. 𝐇𝐲𝐛𝐫𝐢𝐝 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡𝐞𝐬: Combining classical and modern methods can sometimes yield the best results. For instance, using classical methods for pre-processing, such as edge detection or feature extraction before feeding the data into a deep learning model (modern method) can enhance performance and accuracy. This approach leverages the strengths of both classical and modern techniques to achieve better outcomes. For an in-depth exploration of these techniques, please find the full guide attached below. #DataScience #ComputerVision #DeepLearning #ImageProcessing #MachineLearning #AI #ArtificialIntelligence #TechInnovation #BigData #DataScienceCommunity #MachineLearningCommunity #Research #Innovation #AdvancedAnalytics

Data science is a relatively new term that emerged with the rapid advancement of technology and the explosion of available data. It focuses on analyzing large datasets and extracting valuable insights using tools like artificial intelligence and machine learning. It has gained widespread recognition due to its crucial role in decision-making and improving performance across various fields.

In the ever-evolving landscape of data science, the generation of synthetic data has emerged as a vital technique for augmenting datasets, particularly when dealing with sensitive or limited data. Enter the Copula GAN Synthesizer, an innovative tool that blends classical statistical methods with GAN-based deep learning techniques to train models and generate high-quality synthetic data. Key Benefits: Versatility in handling diverse data types. Efficiently manages datasets with missing values. Ensures data privacy while generating realistic synthetic data. At Bluescarf Artificial Intelligence Limited., we're harnessing this technology to enhance data-driven decision-making and innovation across various projects. Stay tuned for more insights on how we are leveraging advanced technologies to drive progress and safeguard data integrity. #SyntheticData #GAN #DataScience #MachineLearning #Bluescarf

📈 What is an Algorithm? An Algorithm is a well-defined sequence of steps or instructions that allows a computer to solve a specific problem or complete a task. In the realm of artificial intelligence, algorithms are the core of machine learning models, enabling machines to analyze data, learn from it, and make decisions. Just like a mathematical function, an algorithm takes one or more inputs and delivers one or more outputs, all within a finite amount of time. 🧠 Algorithms are essential to machine learning, as they power the ability of machines to learn from data without being explicitly programmed. They form the foundation of modern AI systems, driving decision-making processes across industries, from healthcare to finance. 📝 This is part of the glossary I’m developing as I progress through my Master’s in Information Systems, focused on key concepts in Enterprise Architecture and Artificial Intelligence. Stay tuned for more updates as I continue expanding this work! You can check my investigation sources here: Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2022). Introduction to Algorithms, Fourth Edition. MIT Press. 💬 What algorithms are currently driving innovation in your industry? #ArtificialIntelligence #MachineLearning #DeepLearning #NeuralNetworks #TechInnovation #AIinBusiness #FutureOfTech #DataScience #InformationSystems #DigitalTransformation #EnterpriseAI