CastorDoc’s Post

Know the performance of your overall business better by knowing the root cause of any trends in your data through Walmart Luminate Channel Performance Insights! #retail #walmart #walmartluminate #suppliers

🔍 Uncover hidden opportunities and optimize your strategies with Walmart Luminate Charter’s Guided Insights in the enhanced Insights Dashboard.  By harnessing the power of machine learning, Guided Insights takes the guesswork out of identifying underlying issues. Anomaly detection highlights unusual trends and activities, while root cause analysis provides invaluable insights behind your products’ performance. 📈  Ready to gain a comprehensive view of your overall business performance? Watch this video to discover more about Guided Insights in Channel Performance Charter. ⬇️ #WalmartDataVentures #MachineLearning

Generate Synthetic Data Effortlessly with Hugging Face! The Synthetic Data Generator by Hugging Face offers a streamlined way to create synthetic datasets, empowering data scientists and engineers to: 1. Enhance Model Performance: Increase data variety to build robust machine learning models. 2. Protect Privacy: Generate high-quality synthetic data without exposing sensitive information. 3. Accelerate Prototyping: Create labeled datasets quickly for testing new algorithms. 4. Reduce Dependency on Real Data: Eliminate challenges with data scarcity and labeling. Learn more: https://lnkd.in/gcfy2GNc

Here I am presenting Apple's share price trends using a decision tree model in R. I want to thank Dr James Daniel Paul P sir for his guidance. #apple #datascience #R #insights #data #project #learn #grow

Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks Another vision model released, this time by Microsoft. Also worth experimenting with. This is how they describe this new model "We introduce Florence-2, a novel vision foundation model with a unified, prompt-based representation for a variety of computer vision and vision-language tasks. While existing large vision models excel in transfer learning, they struggle to perform a diversity of tasks with simple instructions, a capability that implies handling the complexity of various spatial hierarchy and semantic granularity. Florence-2 was designed to take text-prompt as task instructions and generate desirable results in text forms, whether it be captioning, object detection, grounding or segmentation. " Model is here: https://lnkd.in/eaBRyR7w Paper is here: https://lnkd.in/eSWyZf33

After a long gap, participated in a tabular dataset prediction competition on Kaggle and got a top 5% finish with a place of 125 out of 2746 participants. Tried some new techniques from automated feature engineering with OpenFE to AutoML with FLAML by microsoft, ensembling with model weight optimization using nelder-mead, forward and backward feature selection etc. etc. This is the link to my best solution notebook which scores 0.83778 on private LB with a top 100 finish, which incidentally I didn't select for submission https://lnkd.in/gxFZwVZ5

Learn how LaunchLabs and NineTwoThree Studio turned data challenges into robust solutions, boosting predictive power through machine learning. https://hubs.li/Q02r0SMG0

Data today generally moves around inside computers—and in the case of training AI algorithms, between chips inside a data center—via electrical signals. Read more 👉 https://lttr.ai/ARRJx #AiLeaders #MachineIntelligence #ComputerHardware #FastForward #ArtificialIntelligence #MachineLearning #Tags #Optical—OrPhotonic—InterconnectsBuilt #AppCalledThumbtack #MejulianChokkattuCulturehereS

Microsoft released the Florence 2 model and dataset, advancing small vision models with 200+ and 700+ million parameters. They created 5.4 billion labels from 126 million images for various vision tasks like segmentation and captioning. The model can predict over 10 different tasks per image, including object detection and OCR with regions. Highlights 🧠 Microsoft released Florence 2 model and dataset 🖼️ 200+ and 700+ million parameter models 🏷️ Created 5.4 billion labels for 126 million images 🌟 Model predicts over 10 different vision tasks per image 🚀 Advances in object detection, segmentation, and OCR 🤖 Model fine-tuning for visual question answering 📈 Potential for real-world application and customization

Improve forecast accuracy with AI-powered predictions and historical data. Plug in your own machine-learning models and tailor forecasts using a no-code approach. https://bit.ly/3FL23Dc #supplychainmanagement #msdyn365