Kumar Singh’s Post

In the 4th Episode of "AI@Work", we discuss Churn Analysis. You can watch the episode here: https://lnkd.in/g_y6Ui2j Previous episodes can be watched here: https://lnkd.in/gN8S792v #data #analytics #ai #ml #datascience #designedanalytics

In Episode 2 of AI@Work, we share the framework we will use to cover our video series journey. Then we jump into the first topic: Using AI and ML for customer segmentation. You can watch the episode here: https://lnkd.in/gaYZ8KQQ Previous episode can be watched here: https://lnkd.in/ghyRWf6i #data #analytics #ai #ml #datascience #designedanalytics #deeplearning

In Episode 2 of AI@Work, we share the framework we will use to cover our video series journey. Then we jump into the first topic: Using AI and ML for customer segmentation. You can watch the episode here: https://lnkd.in/g3CSzqsS Previous episode can be watched here: https://lnkd.in/gN8S792v #data #analytics #ai #ml #datascience #designedanalytics #deeplearning

🚀 Excited to share our team's recent project: Diamond Price Prediction 2024! 🚀   Our latest project, developed with my talented team at SHAI For AI | شاي للذكاء الاصطناعي was part of a Kaggle competition hosted by the company. We utilized advanced machine learning techniques to predict diamond prices. By analyzing a rich dataset with attributes like carat weight, cut quality, color, and clarity, we embarked on a comprehensive journey through data preprocessing, exploratory data analysis (EDA), model building, and more.   🔗 For detailed project insights and code, visit my GitHub repository: [ https://lnkd.in/eaE-cQK4 ] - Contains project code, dataset, and documentation.   Special thanks to my incredible teammates: Sarah Hasan Hesham Alsaadi Fahed Shadid for their hard work and dedication.   #DataScience #MachineLearning #PredictiveAnalytics #TeamWork #DiamondPricePrediction

Become an expert on aiXplain’s no-code AI platform and SDK with our #aiXpertTrainingCourse 🤓 In this tutorial, learn to transform your linguistic corpora into structured datasets with aiXplain, enhancing your AI's understanding and performance. Don't forget to subscribe to our YouTube channel for more. 👉 https://lnkd.in/gHCmvZwn #aiXplain #DataScience #Corpus #AI #MachineLearning #LearnAI #AIplatform #AItools

Let’s talk about the challenges AI is facing, and how SPARKDIT can transform it. Watch video for more explanation. #ai #decisionintelligence #makebetterdecisions

#Quiztime⏰⏰ Which of the following is a potential benefit of using GenAI for document analysis? Must Click your answer right away 👇 👇 👇 https://lnkd.in/dahcjwB6

🌱🍎 Celebrating Success: CNN Model Achieves 98% Test Accuracy! 🍅🥦 We're thrilled to announce a major milestone in our Fruits & Vegetables Recognition System journey: our CNN Model with Augmentation has achieved a staggering 98% test accuracy! 🎨 Augmentation Techniques: - Rotation Range - Zoom Range - Width Shift Range - Height Shift Range - Horizontal Flip - Validation Split 🔄 Algorithms Utilized: - CNN Baseline Model - VGG Model With Augmentation - EfficientNet B0 Model Without Augmentation - ResNet Model With Augmentation 🚀 Using Streamlit: Our interface is powered by Streamlit, making it user-friendly and efficient. 🔗 Explore on GitHub: Dive deeper into our project on GitHub: https://lnkd.in/dAMKzCzC 🙌 Team Shoutout: Eman Ramzy, Amina Mohamed, Shorouq Hossam, your unwavering commitment and expertise have propelled us to this incredible feat. Together, we've pushed boundaries and achieved excellence. 🎓 Instructor Appreciation: Eng Abdelrahman Eid, your guidance has been invaluable throughout this journey. Your expertise and encouragement empowered us to reach new heights. 🎥 Coming Soon: Stay tuned for a video showcasing our system in action! #iti #TeamWork #AI #SuccessStory #ArtificialIntelligence #DeepLearning #CNN

Yesterday, my company deepset released a new German embedding model with mixedbread.ai 🚀 deepset-mxbai-embed-de-large-v1 is now available on Hugging Face and you can start using it with #Haystack I wish I spoke German to get the full experience, alas, I do not! But here's a few things to know about it: There are two approaches to make embedding models more efficient with large scale datasets: 1️⃣ Matryoshka representation learning (MRL) 2️⃣ Binary quantization We combine these two for ✨Binary MRL✨: which allows you to reduce model size for improved efficiency, without much performance sacrifices. All in all, they've reported that you could save quite a substatial amount of infrastructure cost 💸 You can read our announcement here: https://lnkd.in/eXyZMzHN Credits to: Madeeswaran Kannan Sebastian H. Aamir Shakir Julius Lipp Darius König Xianming LI

I have written a blog demonstrating real infrastructure Solutions for AI-Powered retrieval more broadly known as RAG. In this step-by-step explanation, I walk you through each component using specific services that best fit my team’s needs. While I highlight particular tools, the principles and approaches can be tailored to suit your preferences and requirements. I hope this will be helpful or at least interesting to you. How do you envision the future applications of RAG in your industry or other industries? #RAG #AI #DataRetrieval #AIInfrastructure #CloudComputing